Photoplethysmography is a key sensing technology which is used in wearable devices such as smartwatches and fitness trackers. Currently, photoplethysmography sensors are used to monitor physiological parameters including heart rate and heart rhythm, and to track activities like sleep and exercise. Yet, wearable photoplethysmography has potential to provide much more information on health and wellbeing, which could inform clinical decision making. This Roadmap outlines directions for research and development to realise the full potential of wearable photoplethysmography. Experts discuss key topics within the areas of sensor design, signal processing, clinical applications, and research directions. Their perspectives provide valuable guidance to researchers developing wearable photoplethysmography technology.
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Physiological Measurement covers the quantitative measurement and visualization of physiological structure and function in clinical research and practice, with an emphasis on the development of new methods of measurement and their validation.
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Peter H Charlton et al 2023 Physiol. Meas. 44 111001
Márton Á Goda et al 2024 Physiol. Meas. 45 045001
Objective. Photoplethysmography is a non-invasive optical technique that measures changes in blood volume within tissues. It is commonly and being increasingly used for a variety of research and clinical applications to assess vascular dynamics and physiological parameters. Yet, contrary to heart rate variability measures, a field which has seen the development of stable standards and advanced toolboxes and software, no such standards and limited open tools exist for continuous photoplethysmogram (PPG) analysis. Consequently, the primary objective of this research was to identify, standardize, implement and validate key digital PPG biomarkers. Approach. This work describes the creation of a standard Python toolbox, denoted pyPPG, for long-term continuous PPG time-series analysis and demonstrates the detection and computation of a high number of fiducial points and digital biomarkers using a standard fingerbased transmission pulse oximeter. Main results. The improved PPG peak detector had an F1-score of 88.19% for the state-of-the-art benchmark when evaluated on 2054 adult polysomnography recordings totaling over 91 million reference beats. The algorithm outperformed the open-source original Matlab implementation by ∼5% when benchmarked on a subset of 100 randomly selected MESA recordings. More than 3000 fiducial points were manually annotated by two annotators in order to validate the fiducial points detector. The detector consistently demonstrated high performance, with a mean absolute error of less than 10 ms for all fiducial points. Significance. Based on these fiducial points, pyPPG engineered a set of 74 PPG biomarkers. Studying PPG time-series variability using pyPPG can enhance our understanding of the manifestations and etiology of diseases. This toolbox can also be used for biomarker engineering in training data-driven models. pyPPG is available on https://physiozoo.com/.
Haipeng Liu et al 2019 Physiol. Meas. 40 07TR01
Respiratory rate (RR) is an important physiological parameter whose abnormality has been regarded as an important indicator of serious illness. In order to make RR monitoring simple to perform, reliable and accurate, many different methods have been proposed for such automatic monitoring. According to the theory of respiratory rate extraction, methods are categorized into three modalities: extracting RR from other physiological signals, RR measurement based on respiratory movements, and RR measurement based on airflow. The merits and limitations of each method are highlighted and discussed. In addition, current works are summarized to suggest key directions for the development of future RR monitoring methodologies.
Hannu Kinnunen et al 2020 Physiol. Meas. 41 04NT01
Objective: To validate the accuracy of the Oura ring in the quantification of resting heart rate (HR) and heart rate variability (HRV). Background: Wearable devices have become comfortable, lightweight, and technologically advanced for assessing health behavior. As an example, the novel Oura ring integrates daily physical activity and nocturnal cardiovascular measurements. Ring users can follow their autonomic nervous system responses to their daily behavior based on nightly changes in HR and HRV, and adjust their behavior accordingly after self-reflection. As wearable photoplethysmogram (PPG) can be disrupted by several confounding influences, it is crucial to demonstrate the accuracy of ring measurements. Approach: Nocturnal HR and HRV were assessed in 49 adults with simultaneous measurements from the Oura ring and the gold standard ECG measurement. Female and male participants with a wide age range (15–72 years) and physical activity status were included. Regression analysis between ECG and the ring outcomes was performed. Main results: Very high agreement between the ring and ECG was observed for nightly average HR and HRV (r2 = 0.996 and 0.980, respectively) with a mean bias of −0.63 bpm and −1.2 ms. High agreement was also observed across 5 min segments within individual nights in (r2 = 0.869 ± 0.098 and 0.765 ± 0.178 in HR and HRV, respectively). Significance: Present findings indicate high validity of the Oura ring in the assessment of nocturnal HR and HRV in healthy adults. The results show the utility of this miniaturised device as a lifestyle management tool in long-term settings. High quality PPG signal results prompt future studies utilizing ring PPG towards clinically relevant health outcomes.
Raghda Al-Halawani et al 2023 Physiol. Meas. 44 05TR01
Objective. Pulse oximetry is a non-invasive optical technique used to measure arterial oxygen saturation (SpO2) in a variety of clinical settings and scenarios. Despite being one the most significant technological advances in health monitoring over the last few decades, there have been reports on its various limitations. Recently due to the Covid-19 pandemic, questions about pulse oximeter technology and its accuracy when used in people with different skin pigmentation have resurfaced, and are to be addressed. Approach. This review presents an introduction to the technique of pulse oximetry including its basic principle of operation, technology, and limitations, with a more in depth focus on skin pigmentation. Relevant literature relating to the performance and accuracy of pulse oximeters in populations with different skin pigmentation are evaluated. Main Results. The majority of the evidence suggests that the accuracy of pulse oximetry differs in subjects of different skin pigmentations to a level that requires particular attention, with decreased accuracy in patients with dark skin. Significance. Some recommendations, both from the literature and contributions from the authors, suggest how future work could address these inaccuracies to potentially improve clinical outcomes. These include the objective quantification of skin pigmentation to replace currently used qualitative methods, and computational modelling for predicting calibration algorithms based on skin colour.
Antti-Pekka E Rissanen et al 2024 Physiol. Meas. 45 055028
Objective. Maximal O2 uptake () reflects the individual's maximal rate of O2 transport and utilization through the integrated whole-body pathway composed of the lungs, heart, blood, circulation, and metabolically active tissues. As such, is strongly associated with physical capacity as well as overall health and thus acts as one predictor of physical performance and as a vital sign in determination of status and progress of numerous clinical conditions. Quantifying the contribution of single parts of the multistep O2 pathway to provides mechanistic insights into exercise (in)tolerance and into therapy-, training-, or disuse-induced adaptations at individual or group levels. We developed a desktop application (Helsinki O2 Pathway Tool—HO2PT) to model numerical and graphical display of the O2 pathway based on the 'Wagner diagram' originally formulated by Peter D. Wagner and his colleagues. Approach. The HO2PT was developed and programmed in Python to integrate the Fick principle and Fick's law of diffusion into a computational system to import, calculate, graphically display, and export variables of the Wagner diagram. Main results. The HO2PT models O2 pathway both numerically and graphically according to the Wagner diagram and pertains to conditions under which the mitochondrial oxidative capacity of metabolically active tissues exceeds the capacity of the O2 transport system to deliver O2 to the mitochondria. The tool is based on the Python open source code and libraries and freely and publicly available online for Windows, macOS, and Linux operating systems. Significance. The HO2PT offers a novel functional and demonstrative platform for those interested in examining and its determinants by using the Wagner diagram. It will improve access to and usability of Wagner's and his colleagues' integrated physiological model and thereby benefit users across the wide spectrum of contexts such as scientific research, education, exercise testing, sports coaching, and clinical medicine.
John M Karemaker 2017 Physiol. Meas. 38 R89
The results of many medical measurements are directly or indirectly influenced by the autonomic nervous system (ANS). For example pupil size or heart rate may demonstrate striking moment-to-moment variability. This review intends to elucidate the physiology behind this seemingly unpredictable system.
The review is split up into: 1. The peripheral ANS, parallel innervation by the sympathetic and parasympathetic branches, their transmitters and co-transmitters. It treats questions like the supposed sympatho/vagal balance, organization in plexuses and the 'little brains' that are active like in the enteric system or around the heart. Part 2 treats ANS-function in some (example-) organs in more detail: the eye, the heart, blood vessels, lungs, respiration and cardiorespiratory coupling. Part 3 poses the question of who is directing what? Is the ANS a strictly top-down directed system or is its organization bottom-up? Finally, it is concluded that the 'noisy numbers' in medical measurements, caused by ANS variability, are part and parcel of how the system works. This topical review is a one-man's undertaking and may possibly give a biased view. The author has explicitly indicated in the text where his views are not (yet) supported by facts, hoping to provoke discussion and instigate new research.
Huy Phan and Kaare Mikkelsen 2022 Physiol. Meas. 43 04TR01
Modern deep learning holds a great potential to transform clinical studies of human sleep. Teaching a machine to carry out routine tasks would be a tremendous reduction in workload for clinicians. Sleep staging, a fundamental step in sleep practice, is a suitable task for this and will be the focus in this article. Recently, automatic sleep-staging systems have been trained to mimic manual scoring, leading to similar performance to human sleep experts, at least on scoring of healthy subjects. Despite tremendous progress, we have not seen automatic sleep scoring adopted widely in clinical environments. This review aims to provide the shared view of the authors on the most recent state-of-the-art developments in automatic sleep staging, the challenges that still need to be addressed, and the future directions needed for automatic sleep scoring to achieve clinical value.
Santtu M Seipäjärvi et al 2022 Physiol. Meas. 43 055002
Objective. Autonomic nervous system function and thereby bodily stress and recovery reactions may be assessed by wearable devices measuring heart rate (HR) and its variability (HRV). So far, the validity of HRV-based stress assessments has been mainly studied in healthy populations. In this study, we determined how psychosocial stress affects physiological and psychological stress responses in both young (18–30 years) and middle-aged (45–64 years) healthy individuals as well as in patients with arterial hypertension and/or either prior evidence of prediabetes or type 2 diabetes. We also studied how an HRV-based stress index (Relax-Stress Intensity, RSI) relates to perceived stress (PS) and cortisol (CRT) responses during psychosocial stress. Approach. A total of 197 participants were divided into three groups: (1) healthy young (HY, N = 63), (2) healthy middle-aged (HM, N = 61) and (3) patients with cardiometabolic risk factors (Pts, N = 73, 32–65 years). The participants underwent a group version of Trier Social Stress Test (TSST-G). HR, HRV (quantified as root mean square of successive differences of R–R intervals, RMSSD), RSI, PS, and salivary CRT were measured regularly during TSST-G and a subsequent recovery period. Main results. All groups showed significant stress reactions during TSST-G as indicated by significant responses of HR, RMSSD, RSI, PS, and salivary CRT. Between-group differences were also observed in all measures. Correlation and regression analyses implied RSI being the strongest predictor of CRT response, while HR was more closely associated with PS. Significance. The HRV-based stress index mirrors responses of CRT, which is an independent marker for physiological stress, around TSST-G. Thus, the HRV-based stress index may be used to quantify physiological responses to psychosocial stress across various health and age groups.
Peter H Charlton et al 2022 Physiol. Meas. 43 085007
The photoplethysmogram (PPG) signal is widely used in pulse oximeters and smartwatches. A fundamental step in analysing the PPG is the detection of heartbeats. Several PPG beat detection algorithms have been proposed, although it is not clear which performs best. Objective: This study aimed to: (i) develop a framework with which to design and test PPG beat detectors; (ii) assess the performance of PPG beat detectors in different use cases; and (iii) investigate how their performance is affected by patient demographics and physiology. Approach: Fifteen beat detectors were assessed against electrocardiogram-derived heartbeats using data from eight datasets. Performance was assessed using the F1 score, which combines sensitivity and positive predictive value. Main results: Eight beat detectors performed well in the absence of movement with F1 scores of ≥90% on hospital data and wearable data collected at rest. Their performance was poorer during exercise with F1 scores of 55%–91%; poorer in neonates than adults with F1 scores of 84%–96% in neonates compared to 98%–99% in adults; and poorer in atrial fibrillation (AF) with F1 scores of 92%–97% in AF compared to 99%–100% in normal sinus rhythm. Significance: Two PPG beat detectors denoted 'MSPTD' and 'qppg' performed best, with complementary performance characteristics. This evidence can be used to inform the choice of PPG beat detector algorithm. The algorithms, datasets, and assessment framework are freely available.
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Jingwei Zhang et al 2024 Physiol. Meas. 45 065005
Objective. This paper aims to investigate the possibility of detecting tonic-clonic seizures (TCSs) with behind-the-ear, two-channel wearable electroencephalography (EEG), and to evaluate its added value to non-EEG modalities in TCS detection. Methods. We included 27 participants with a total of 44 TCSs from the European multicenter study SeizeIT2. The wearable Sensor Dot (Byteflies) was used to measure behind-the-ear EEG, electromyography (EMG), electrocardiography, accelerometry (ACC) and gyroscope. We evaluated automatic unimodal detection of TCSs, using sensitivity, precision, false positive rate (FPR) and F1-score. Subsequently, we fused the different modalities and again assessed performance. Algorithm-labeled segments were then provided to two experts, who annotated true positive TCSs, and discarded false positives. Results. Wearable EEG outperformed the other single modalities with a sensitivity of 100% and a FPR of 10.3/24 h. The combination of wearable EEG and EMG proved most clinically useful, delivering a sensitivity of 97.7%, an FPR of 0.4/24 h, a precision of 43%, and an F1-score of 59.7%. The highest overall performance was achieved through the fusion of wearable EEG, EMG, and ACC, yielding a sensitivity of 90.9%, an FPR of 0.1/24 h, a precision of 75.5%, and an F1-score of 82.5%. Conclusions. In TCS detection with a wearable device, combining EEG with EMG, ACC or both resulted in a remarkable reduction of FPR, while retaining a high sensitivity. Significance. Adding wearable EEG could further improve TCS detection, relative to extracerebral-based systems.
Jessica Keim-Malpass et al 2024 Physiol. Meas. 45 065004
Objective. Very few predictive models have been externally validated in a prospective cohort following the implementation of an artificial intelligence analytic system. This type of real-world validation is critically important due to the risk of data drift, or changes in data definitions or clinical practices over time, that could impact model performance in contemporaneous real-world cohorts. In this work, we report the model performance of a predictive analytics tool developed before COVID-19 and demonstrate model performance during the COVID-19 pandemic. Approach. The analytic system (CoMETⓇ, Nihon Kohden Digital Health Solutions LLC, Irvine, CA) was implemented in a randomized controlled trial that enrolled 10 422 patient visits in a 1:1 display-on display-off design. The CoMET scores were calculated for all patients but only displayed in the display-on arm. Only the control/display-off group is reported here because the scores could not alter care patterns. Main results. Of the 5184 visits in the display-off arm, 311 experienced clinical deterioration and care escalation, resulting in transfer to the intensive care unit, primarily due to respiratory distress. The model performance of CoMET was assessed based on areas under the receiver operating characteristic curve, which ranged from 0.725 to 0.737. Significance. The models were well-calibrated, and there were dynamic increases in the model scores in the hours preceding the clinical deterioration events. A hypothetical alerting strategy based on a rise in score and duration of the rise would have had good performance, with a positive predictive value more than 10-fold the event rate. We conclude that predictive statistical models developed five years before study initiation had good model performance despite the passage of time and the impact of the COVID-19 pandemic.
Longfei Liu et al 2024 Physiol. Meas. 45 065003
Objective. Acute hypotension episode (AHE) is one of the most critical complications in intensive care unit (ICU). A timely and precise AHE prediction system can provide clinicians with sufficient time to respond with proper therapeutic measures, playing a crucial role in saving patients' lives. Recent studies have focused on utilizing more complex models to improve predictive performance. However, these models are not suitable for clinical application due to limited computing resources for bedside monitors. Approach. To address this challenge, we propose an efficient lightweight dilated shuffle group network. It effectively incorporates shuffling operations into grouped convolutions on the channel and dilated convolutions on the temporal dimension, enhancing global and local feature extraction while reducing computational load. Main results. Our benchmarking experiments on the MIMIC-III and VitalDB datasets, comprising 6036 samples from 1304 patients and 2958 samples from 1047 patients, respectively, demonstrate that our model outperforms other state-of-the-art lightweight CNNs in terms of balancing parameters and computational complexity. Additionally, we discovered that the utilization of multiple physiological signals significantly improves the performance of AHE prediction. External validation on the MIMIC-IV dataset confirmed our findings, with prediction accuracy for AHE 5 min prior reaching 93.04% and 92.04% on the MIMIC-III and VitalDB datasets, respectively, and 89.47% in external verification. Significance. Our study demonstrates the potential of lightweight CNN architectures in clinical applications, providing a promising solution for real-time AHE prediction under resource constraints in ICU settings, thereby marking a significant step forward in improving patient care.
Kai Mason et al 2024 Physiol. Meas. 45 065002
Objective. Noisy measurements frequently cause noisy and inaccurate images in impedance imaging. No post-processing technique exists to calculate the propagation of measurement noise and use this to suppress noise in the image. The objectives of this work were (1) to develop a post-processing method for noise-based correction (NBC) in impedance tomography, (2) to test whether NBC improves image quality in electrical impedance tomography (EIT), (3) to determine whether it is preferable to use correlated or uncorrelated noise for NBC, (4) to test whether NBC works with in vivo data and (5) to test whether NBC is stable across model and perturbation geometries. Approach. EIT was performed in silico in a 2D homogeneous circular domain and an anatomically realistic, heterogeneous 3D human head domain for four perturbations and 25 noise levels in each case. This was validated by performing EIT for four perturbations in a circular, saline tank in 2D as well as a human head-shaped saline tank with a realistic skull-like layer in 3D. Images were assessed on the error in the weighted spatial variance (WSV) with respect to the true, target image. The effect of NBC was also tested for in vivo EIT data of lung ventilation in a human thorax and cortical activity in a rat brain. Main results. On visual inspection, NBC maintained or increased image quality for all perturbations and noise levels in 2D and 3D, both experimentally and in silico. Analysis of the WSV showed that NBC significantly improved the WSV in nearly all cases. When the WSV was inferior with NBC, this was either visually imperceptible or a transformation between noisy reconstructions. For in vivo data, NBC improved image quality in all cases and preserved the expected shape of the reconstructed perturbation. Significance. In practice, uncorrelated NBC performed better than correlated NBC and is recommended as a general-use post-processing technique in EIT.
Branko G Celler and Ahmadreza Argha 2024 Physiol. Meas. 45 055027
Objectives. In this study, we test the hypothesis that if, as demonstrated in a previous study, brachial arteries exhibit hysteresis as the occluding cuff is deflated and fail to open until cuff pressure (CP) is well below true intra-arterial blood pressure (IAPB), estimating systolic (SBP) and diastolic blood pressure (DBP) from the presence of Korotkoff sounds (KS) as CP increases may eliminate these errors and give more accurate estimates of SBP and DBP relative to IABP readings. Approach. In 62 subjects of varying ages (45.1 ± 19.8, range 20.6–75.8 years), including 44 men (45.3 ± 19.4, range 20.6–75.8 years) and 18 women (44.4 ± 21.4, range 20.9–75.3 years), we sequentially recorded SBP and DBP both during cuff inflation and cuff deflation using KS. Results. There was a significant (p < 0.0001) increase in SBP from 122.8 ± 13.2 to 127.6 ± 13.0 mmHg and a significant (p = 0.0001) increase in DBP from 70.0 ± 9.0 to 77.5 ± 9.7 mmHg. Of the 62 subjects, 51 showed a positive increase in SBP (0–14 mmHg) and 11 subjects showed a reduction (−0.3 to −7 mmHg). The average differences for SBP and DBP estimates derived as the cuff inflates and those derived as the cuff deflates were 4.8 ± 4.6 mmHg and 2.5 ± 4.6 mmHg, not dissimilar to the differences reported between IABP and non-invasive blood pressure measurements. Although we could not develop multiparameter linear or non-linear models to explain this phenomenon we have clearly demonstrated through ANOVA tests that both body mass index (BMI) and pulse wave velocity are implicated, supporting the hypothesis that the phenomenon is associated with age, higher BMI and stiffer arteries. Significance. The implications of this study are that brachial sphygmomanometry carried out during cuff inflation could be more accurate than measurements carried out as the cuff deflates. Further research is required to validate these results with IAPB measurements.
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Nürfet Balkan et al 2024 Physiol. Meas. 45 05TR01
Objective. The physiological, hormonal and biomechanical changes during pregnancy may trigger sleep disordered breathing (SDB) in pregnant women. Pregnancy-related sleep disorders may associate with adverse fetal and maternal outcomes including gestational diabetes, preeclampsia, preterm birth and gestational hypertension. Most of the screening and diagnostic studies that explore SDB during pregnancy were based on questionnaires which are inherently limited in providing definitive conclusions. The current gold standard in diagnostics is overnight polysomnography (PSG) involving the comprehensive measurements of physiological changes during sleep. However, applying the overnight laboratory PSG on pregnant women is not practical due to a number of challenges such as patient inconvenience, unnatural sleep dynamics, and expenses due to highly trained personnel and technology. Parallel to the progress in wearable sensors and portable electronics, home sleep monitoring devices became indispensable tools to record the sleep signals of pregnant women at her own sleep environment. This article reviews the application of portable sleep monitoring devices in pregnancy with particular emphasis on estimating the perinatal outcomes. Approach. The advantages and disadvantages of home based sleep monitoring systems compared to subjective sleep questionnaires and overnight PSG for pregnant women were evaluated. Main Results. An overview on the efficiency of the application of home sleep monitoring in terms of accuracy and specificity were presented for particular fetal and maternal outcomes. Significance. Based on our review, more homogenous and comparable research is needed to produce conclusive results with home based sleep monitoring systems to study the epidemiology of SDB in pregnancy and its impact on maternal and neonatal health.
Cheng Ding et al 2024 Physiol. Meas. 45 04TR01
Objective. Atrial fibrillation (AF) is a prevalent cardiac arrhythmia associated with significant health ramifications, including an elevated susceptibility to ischemic stroke, heart disease, and heightened mortality. Photoplethysmography (PPG) has emerged as a promising technology for continuous AF monitoring for its cost-effectiveness and widespread integration into wearable devices. Our team previously conducted an exhaustive review on PPG-based AF detection before June 2019. However, since then, more advanced technologies have emerged in this field. Approach. This paper offers a comprehensive review of the latest advancements in PPG-based AF detection, utilizing digital health and artificial intelligence (AI) solutions, within the timeframe spanning from July 2019 to December 2022. Through extensive exploration of scientific databases, we have identified 57 pertinent studies. Significance. Our comprehensive review encompasses an in-depth assessment of the statistical methodologies, traditional machine learning techniques, and deep learning approaches employed in these studies. In addition, we address the challenges encountered in the domain of PPG-based AF detection. Furthermore, we maintain a dedicated website to curate the latest research in this area, with regular updates on a regular basis.
Liqing Yang et al 2024 Physiol. Meas. 45 03TR02
Background and Objective. Sleep-disordered breathing (SDB) poses health risks linked to hypertension, cardiovascular disease, and diabetes. However, the time-consuming and costly standard diagnostic method, polysomnography (PSG), limits its wide adoption and leads to underdiagnosis. To tackle this, cost-effective algorithms using single-lead signals (like respiratory, blood oxygen, and electrocardiogram) have emerged. Despite respiratory signals being preferred for SDB assessment, a lack of comprehensive reviews addressing their algorithmic scope and performance persists. This paper systematically reviews 2012–2022 literature, covering signal sources, processing, feature extraction, classification, and application, aiming to bridge this gap and provide future research references. Methods. This systematic review followed the registered PROSPERO protocol (CRD42022385130), initially screening 342 papers, with 32 studies meeting data extraction criteria. Results. Respiratory signal sources include nasal airflow (NAF), oronasal airflow (OAF), and respiratory movement-related signals such as thoracic respiratory effort (TRE) and abdominal respiratory effort (ARE). Classification techniques include threshold rule-based methods (8), machine learning models (13), and deep learning models (11). The NAF-based algorithm achieved the highest average accuracy at 94.11%, surpassing 78.19% for other signals. Hypopnea detection sensitivity with single-source respiratory signals remained modest, peaking at 73.34%. The TRE and ARE signals proved to be reliable in identifying different types of SDB because distinct respiratory disorders exhibited different patterns of chest and abdominal motion. Conclusions. Multiple detection algorithms have been widely applied for SDB detection, and their accuracy is closely related to factors such as signal source, signal processing, feature selection, and model selection.
Manisha Ingle et al 2024 Physiol. Meas. 45 03TR01
Background. Insomnia is a prevalent sleep disorder characterized by difficulties in initiating sleep or experiencing non-restorative sleep. It is a multifaceted condition that impacts both the quantity and quality of an individual's sleep. Recent advancements in machine learning (ML), and deep learning (DL) have enabled automated sleep analysis using physiological signals. This has led to the development of technologies for more accurate detection of various sleep disorders, including insomnia. This paper explores the algorithms and techniques for automatic insomnia detection. Methods. We followed the recommendations given in the Preferred Reporting Items for systematic reviews and meta-analyses (PRISMA) during our process of content discovery. Our review encompasses research papers published between 2015 and 2023, with a specific emphasis on automating the identification of insomnia. From a selection of well-regarded journals, we included more than 30 publications dedicated to insomnia detection. In our analysis, we assessed the performance of various methods for detecting insomnia, considering different datasets and physiological signals. A common thread across all the papers we reviewed was the utilization of artificial intelligence (AI) models, trained and tested using annotated physiological signals. Upon closer examination, we identified the utilization of 15 distinct algorithms for this detection task. Results. The major goal of this research is to conduct a thorough study to categorize, compare, and assess the key traits of automated systems for identifying insomnia. Our analysis offers complete and in-depth information. The essential components under investigation in the automated technique include the data input source, objective, ML and DL network, training framework, and references to databases. We classified pertinent research studies based on ML and DL model perspectives, considering factors like learning structure and input data types. Conclusion. Based on our review of the studies featured in this paper, we have identified a notable research gap in the current methods for identifying insomnia and opportunities for future advancements in the automation of insomnia detection. While the current techniques have shown promising results, there is still room for improvement in terms of accuracy and reliability. Future developments in technology and machine learning algorithms could help address these limitations and enable more effective and efficient identification of insomnia.
Leandro Narciso Santiago et al 2024 Physiol. Meas. 45 02TR02
Introduction. Bioelectrical impedance vector analysis (BIVA) emerges as a technique that utilizes raw parameters of bioelectrical impedance analysis and assumes the use of a reference population for information analysis. Objective. To summarize the reference values, main studies objectives, approaches, pre-test recommendations and technical characteristics of the devices employed in studies utilizing BIVA among children and adolescents without diagnosed diseases. Methods. A systematic search was conducted in nine electronic databases (CINAHL, LILACS, PubMed, SciELO, Scopus, SPORTDiscus, Science Direct, MEDLINE, and Web of Science). Studies with different designs which allowed extracting information regarding reference values of BIVA in children and adolescents without diagnosed diseases, aged 19 years or younger, were included. The systematic review followed PRISMA procedures and was registered in PROSPERO (registration: CRD42023391069). Results. After applying the eligibility criteria, 36 studies were included. Twenty studies (55.6%) analyzed body composition using BIVA, thirteen studies (36.1%) aimed to establish reference values for BIVA, and three studies (8.3%) investigated the association of physical performance with BIVA. There was heterogeneity regarding the reference populations employed by the studies. Fifteen studies used their own sample as a reference (41.6%), four studies used the adult population as a reference (11.1%), and five studies used reference values from athletes (13.9%). Conclusion. Nutricional status and body composition were the main studies objectives. References values were not always adequate or specific for the sample and population. Furthermore, there was no pattern of pre-test recommendations among the studies.
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Gielen et al
Objective: The fact that ramp incremental exercise yields quasi-linear responses for pulmonary oxygen uptake (O2) and heart rate (HR) seems contradictory to the well-known non-linear behavior of underlying physiological processes. Prior research highlights this issue and demonstrates how a balancing of system gain and response time parameters causes linear O2 responses during ramp tests. This study builds upon this knowledge and extracts the time-varying dynamics directly from HR and O2 data of single ramp incremental running tests. Approach: A large-scale open access dataset of 735 ramp incremental running tests is analysed. The dynamics are obtained by means of 1st order ARX models with time-variant parameters. This allows for the estimates of time constant (τ) and steady state gain (SSG) to vary with work rate. Main results: As the work rate increases, τ-values increase on average from 38 to 132 seconds for HR, and from 27 to 35 seconds for O2. Both increases are statistically significant (p < 0.01). Further, SSG-values decrease on average from 14 to 9 bpm/(km.h-1) for HR, and from 218 to 144 mL.min-1 for O2 (p < 0.01 for decrease parameters of HR and O2). The results of this modelling approach are line with literature reporting on cardiorespiratory dynamics obtained using standard procedures. Significance: We show that time-variant modelling is able to determine the time-varying dynamics HR and O2 responses to ramp incremental running directly from individual tests. The proposed method allows for gaining insights into the cardiorespiratory response characteristics when no repeated measurements are available.
Gregoire et al
Objective
This study examines the value of ventricular repolarization using QT dynamicity for two different types of atrial fibrillation (AF) prediction. 
Approach
We studied the importance of QT-dynamicity 1) in the detection and 2) the onset prediction (i.e. forecasting) of paroxysmal AF episodes using gradient-boosted decision trees (GBDT), an interpretable machine learning technique. We labeled 176 paroxysmal AF onsets from 88 patients in our unselected Holter recordings database containing paroxysmal AF episodes. Raw ECG signals were delineated using a wavelet-based signal processing technique. A total of 44 ECG features related to interval and wave durations and amplitude were selected. and the GBDT model was trained with a Bayesian hyperparameters selection for various windows. 
Main results
The mean age of the patients was 75.9±11.9 (range 50-99), the number of episodes per patient was 2.3±2.2 (range 1-11), and CHA2DS2-VASc score was 2.9±1.7 (range 1-9). For the detection of AF, we obtained an area under the receiver operating curve (AUROC) of 0.99 (CI 95% 0.98 - 0.99) using a 30s window. Features related to RR intervals were the most influential, followed by those on QT intervals. For the AF onset forecast, we obtained an AUROC of 0.739 (0.712-0.766) using a 120s window. R wave amplitude and QT dynamicity as assessed by Spearman's correlation of the QT-RR slope were the best predictors. 
Significance
The QT dynamicity can be used to accurately predict the onset of AF episodes. Ventricular repolarization, as assessed by QT dynamicity, adds information that allows for better short time prediction of AF onset, compared to relying only on RR intervals and HRV. Communication between the ventricles and atria is mediated by the autonomic nervous system. The variations in intraventricular conduction and ventricular repolarization changes resulting from the influence of the ANS play a role in the initiation of AF.
Moreno et al
Objective:
To compare the acute psychophysiological responses to blood flow restriction (BFR) exercise using a traditional research device or novel, automated system.
Methods:
Forty-four resistance trained individuals performed four sets of unilateral elbow flexion exercise [30% one-repetition maximum (1RM)] to volitional failure using two distinct restrictive devices [SmartCuffs PRO BFR Model (SMARTCUFF), Hokanson E20 Rapid Inflation device (HOKANSON)] and with two levels of BFR [40% limb occlusion pressure (LOP), 80% LOP]. Blood pressure (BP), muscle thickness (MT), and isometric strength (ISO) were assessed prior to and following exercise. Perceptual responses [ratings of perceived exertion (RPE), discomfort] were assessed prior to exercise and following each exercise set.
Main Results:
Data are displayed as means (SD). Immediately following exercise with 40% LOP, there were no statistical differences between devices for BP, MT, and ISO. However, only following Set 1 of exercise, RPE was greater with SMARTCUFF compared to HOKANSON (p < 0.05). In addition, only following Set 2 of exercise, discomfort was greater with HOKANSON compared to SMARTCUFF (p < 0.001). Immediately following exercise with 80% LOP, there were no statistical differences between devices for BP, MT, and ISO. However, only following Set 4 of exercise, RPE was greater with HOKANSON compared to SMARTCUFF (p < 0.05). In addition, following all exercise sets, discomfort was greater with HOKANSON compared to SMARTCUFF (p < 0.001). Significance: The present study provides valuable insight into the efficacy of a novel, automated BFR system (SMARTCUFF) eliciting comparable acute physiological responses to BFR exercise and in some cases favorable psychological responses when compared to a traditional research device (HOKANSON).
Panerai et al
Objective. Cerebral critical closing pressure (CrCP) represents the value of arterial blood pressure (BP) where cerebral blood flow (CBF) becomes zero. Its dynamic response to a step change in mean BP (MAP) has been shown to reflect CBF autoregulation, but robust methods for its estimation are lacking. We aim to improve the quality of estimates of the CrCP dynamic response. Approach. Retrospective analysis of 437 healthy subjects (aged 18-87 years, 218 males) baseline recordings with measurements of cerebral blood velocity in the middle cerebral artery (MCAv, transcranial Doppler), non-invasive arterial BP (Finometer) and end-tidal CO2 (EtCO2, capnography). For each cardiac cycle CrCP was estimated from the instantaneous MCAv-BP relationship. Transfer function analysis of the MAP and MCAv (MAP-MCAv) and CrCP (MAP-CrCP) allowed estimation of the corresponding step responses (SR) to changes in MAP, with the output in MCAv (SRVMCAv) representing the autoregulation index (ARI), ranging from 0 to 9. Four main parameters were considered as potential determinants of the SRVCrCP temporal pattern, including the coherence function, MAP spectral power and the reconstruction error for SRVMAP, from the other three separate SRs. Main results. The reconstruction error for SRVMAP was the main determinant of SRVCrCP signal quality, by removing the largest number of outliers (Grubbs test) compared to the other three parameters. SRVCrCP showed highly significant (p<0.001) changes with time, but its amplitude or temporal pattern was not influenced by sex or age. The main physiological determinants of SRVCrCP were the ARI and the mean CrCP for the entire five-minute baseline period. The early phase (2-3 s) of SRVCrCP response was influenced by heart rate whereas the late phase (10-14 s) was influenced by diastolic BP. Significance. These results should allow better planning and quality of future research and clinical trials of novel metrics of cerebral blood flow regulation.
Zanelli et al
Vascular ageing is the deterioration of arterial structure and function which occurs naturally with age, and which can be accelerated with disease. Measurements of vascular ageing are emerging as markers of cardiovascular risk, with potential applications in disease diagnosis and prognosis, and for guiding treatments. However, vascular ageing is not yet routinely assessed in clinical practice. A key step towards this is the development of technologies to assess vascular ageing. In this Roadmap, experts discuss several aspects of this process, including: measurement technologies; the development pipeline; clinical applications; and future research directions. The Roadmap summarises the state of the art, outlines the major challenges to overcome, and identifies potential future research directions to address these challenges.
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Jessica Keim-Malpass et al 2024 Physiol. Meas. 45 065004
Objective. Very few predictive models have been externally validated in a prospective cohort following the implementation of an artificial intelligence analytic system. This type of real-world validation is critically important due to the risk of data drift, or changes in data definitions or clinical practices over time, that could impact model performance in contemporaneous real-world cohorts. In this work, we report the model performance of a predictive analytics tool developed before COVID-19 and demonstrate model performance during the COVID-19 pandemic. Approach. The analytic system (CoMETⓇ, Nihon Kohden Digital Health Solutions LLC, Irvine, CA) was implemented in a randomized controlled trial that enrolled 10 422 patient visits in a 1:1 display-on display-off design. The CoMET scores were calculated for all patients but only displayed in the display-on arm. Only the control/display-off group is reported here because the scores could not alter care patterns. Main results. Of the 5184 visits in the display-off arm, 311 experienced clinical deterioration and care escalation, resulting in transfer to the intensive care unit, primarily due to respiratory distress. The model performance of CoMET was assessed based on areas under the receiver operating characteristic curve, which ranged from 0.725 to 0.737. Significance. The models were well-calibrated, and there were dynamic increases in the model scores in the hours preceding the clinical deterioration events. A hypothetical alerting strategy based on a rise in score and duration of the rise would have had good performance, with a positive predictive value more than 10-fold the event rate. We conclude that predictive statistical models developed five years before study initiation had good model performance despite the passage of time and the impact of the COVID-19 pandemic.
Kai Mason et al 2024 Physiol. Meas. 45 065002
Objective. Noisy measurements frequently cause noisy and inaccurate images in impedance imaging. No post-processing technique exists to calculate the propagation of measurement noise and use this to suppress noise in the image. The objectives of this work were (1) to develop a post-processing method for noise-based correction (NBC) in impedance tomography, (2) to test whether NBC improves image quality in electrical impedance tomography (EIT), (3) to determine whether it is preferable to use correlated or uncorrelated noise for NBC, (4) to test whether NBC works with in vivo data and (5) to test whether NBC is stable across model and perturbation geometries. Approach. EIT was performed in silico in a 2D homogeneous circular domain and an anatomically realistic, heterogeneous 3D human head domain for four perturbations and 25 noise levels in each case. This was validated by performing EIT for four perturbations in a circular, saline tank in 2D as well as a human head-shaped saline tank with a realistic skull-like layer in 3D. Images were assessed on the error in the weighted spatial variance (WSV) with respect to the true, target image. The effect of NBC was also tested for in vivo EIT data of lung ventilation in a human thorax and cortical activity in a rat brain. Main results. On visual inspection, NBC maintained or increased image quality for all perturbations and noise levels in 2D and 3D, both experimentally and in silico. Analysis of the WSV showed that NBC significantly improved the WSV in nearly all cases. When the WSV was inferior with NBC, this was either visually imperceptible or a transformation between noisy reconstructions. For in vivo data, NBC improved image quality in all cases and preserved the expected shape of the reconstructed perturbation. Significance. In practice, uncorrelated NBC performed better than correlated NBC and is recommended as a general-use post-processing technique in EIT.
Enrique N Moreno et al 2024 Physiol. Meas.
Objective:
To compare the acute psychophysiological responses to blood flow restriction (BFR) exercise using a traditional research device or novel, automated system.
Methods:
Forty-four resistance trained individuals performed four sets of unilateral elbow flexion exercise [30% one-repetition maximum (1RM)] to volitional failure using two distinct restrictive devices [SmartCuffs PRO BFR Model (SMARTCUFF), Hokanson E20 Rapid Inflation device (HOKANSON)] and with two levels of BFR [40% limb occlusion pressure (LOP), 80% LOP]. Blood pressure (BP), muscle thickness (MT), and isometric strength (ISO) were assessed prior to and following exercise. Perceptual responses [ratings of perceived exertion (RPE), discomfort] were assessed prior to exercise and following each exercise set.
Main Results:
Data are displayed as means (SD). Immediately following exercise with 40% LOP, there were no statistical differences between devices for BP, MT, and ISO. However, only following Set 1 of exercise, RPE was greater with SMARTCUFF compared to HOKANSON (p < 0.05). In addition, only following Set 2 of exercise, discomfort was greater with HOKANSON compared to SMARTCUFF (p < 0.001). Immediately following exercise with 80% LOP, there were no statistical differences between devices for BP, MT, and ISO. However, only following Set 4 of exercise, RPE was greater with HOKANSON compared to SMARTCUFF (p < 0.05). In addition, following all exercise sets, discomfort was greater with HOKANSON compared to SMARTCUFF (p < 0.001). Significance: The present study provides valuable insight into the efficacy of a novel, automated BFR system (SMARTCUFF) eliciting comparable acute physiological responses to BFR exercise and in some cases favorable psychological responses when compared to a traditional research device (HOKANSON).
Ronney B Panerai et al 2024 Physiol. Meas.
Objective. Cerebral critical closing pressure (CrCP) represents the value of arterial blood pressure (BP) where cerebral blood flow (CBF) becomes zero. Its dynamic response to a step change in mean BP (MAP) has been shown to reflect CBF autoregulation, but robust methods for its estimation are lacking. We aim to improve the quality of estimates of the CrCP dynamic response. Approach. Retrospective analysis of 437 healthy subjects (aged 18-87 years, 218 males) baseline recordings with measurements of cerebral blood velocity in the middle cerebral artery (MCAv, transcranial Doppler), non-invasive arterial BP (Finometer) and end-tidal CO2 (EtCO2, capnography). For each cardiac cycle CrCP was estimated from the instantaneous MCAv-BP relationship. Transfer function analysis of the MAP and MCAv (MAP-MCAv) and CrCP (MAP-CrCP) allowed estimation of the corresponding step responses (SR) to changes in MAP, with the output in MCAv (SRVMCAv) representing the autoregulation index (ARI), ranging from 0 to 9. Four main parameters were considered as potential determinants of the SRVCrCP temporal pattern, including the coherence function, MAP spectral power and the reconstruction error for SRVMAP, from the other three separate SRs. Main results. The reconstruction error for SRVMAP was the main determinant of SRVCrCP signal quality, by removing the largest number of outliers (Grubbs test) compared to the other three parameters. SRVCrCP showed highly significant (p<0.001) changes with time, but its amplitude or temporal pattern was not influenced by sex or age. The main physiological determinants of SRVCrCP were the ARI and the mean CrCP for the entire five-minute baseline period. The early phase (2-3 s) of SRVCrCP response was influenced by heart rate whereas the late phase (10-14 s) was influenced by diastolic BP. Significance. These results should allow better planning and quality of future research and clinical trials of novel metrics of cerebral blood flow regulation.
Serena Zanelli et al 2024 Physiol. Meas.
Vascular ageing is the deterioration of arterial structure and function which occurs naturally with age, and which can be accelerated with disease. Measurements of vascular ageing are emerging as markers of cardiovascular risk, with potential applications in disease diagnosis and prognosis, and for guiding treatments. However, vascular ageing is not yet routinely assessed in clinical practice. A key step towards this is the development of technologies to assess vascular ageing. In this Roadmap, experts discuss several aspects of this process, including: measurement technologies; the development pipeline; clinical applications; and future research directions. The Roadmap summarises the state of the art, outlines the major challenges to overcome, and identifies potential future research directions to address these challenges.
Branko G Celler and Ahmadreza Argha 2024 Physiol. Meas. 45 055027
Objectives. In this study, we test the hypothesis that if, as demonstrated in a previous study, brachial arteries exhibit hysteresis as the occluding cuff is deflated and fail to open until cuff pressure (CP) is well below true intra-arterial blood pressure (IAPB), estimating systolic (SBP) and diastolic blood pressure (DBP) from the presence of Korotkoff sounds (KS) as CP increases may eliminate these errors and give more accurate estimates of SBP and DBP relative to IABP readings. Approach. In 62 subjects of varying ages (45.1 ± 19.8, range 20.6–75.8 years), including 44 men (45.3 ± 19.4, range 20.6–75.8 years) and 18 women (44.4 ± 21.4, range 20.9–75.3 years), we sequentially recorded SBP and DBP both during cuff inflation and cuff deflation using KS. Results. There was a significant (p < 0.0001) increase in SBP from 122.8 ± 13.2 to 127.6 ± 13.0 mmHg and a significant (p = 0.0001) increase in DBP from 70.0 ± 9.0 to 77.5 ± 9.7 mmHg. Of the 62 subjects, 51 showed a positive increase in SBP (0–14 mmHg) and 11 subjects showed a reduction (−0.3 to −7 mmHg). The average differences for SBP and DBP estimates derived as the cuff inflates and those derived as the cuff deflates were 4.8 ± 4.6 mmHg and 2.5 ± 4.6 mmHg, not dissimilar to the differences reported between IABP and non-invasive blood pressure measurements. Although we could not develop multiparameter linear or non-linear models to explain this phenomenon we have clearly demonstrated through ANOVA tests that both body mass index (BMI) and pulse wave velocity are implicated, supporting the hypothesis that the phenomenon is associated with age, higher BMI and stiffer arteries. Significance. The implications of this study are that brachial sphygmomanometry carried out during cuff inflation could be more accurate than measurements carried out as the cuff deflates. Further research is required to validate these results with IAPB measurements.
Antti-Pekka E Rissanen et al 2024 Physiol. Meas. 45 055028
Objective. Maximal O2 uptake () reflects the individual's maximal rate of O2 transport and utilization through the integrated whole-body pathway composed of the lungs, heart, blood, circulation, and metabolically active tissues. As such, is strongly associated with physical capacity as well as overall health and thus acts as one predictor of physical performance and as a vital sign in determination of status and progress of numerous clinical conditions. Quantifying the contribution of single parts of the multistep O2 pathway to provides mechanistic insights into exercise (in)tolerance and into therapy-, training-, or disuse-induced adaptations at individual or group levels. We developed a desktop application (Helsinki O2 Pathway Tool—HO2PT) to model numerical and graphical display of the O2 pathway based on the 'Wagner diagram' originally formulated by Peter D. Wagner and his colleagues. Approach. The HO2PT was developed and programmed in Python to integrate the Fick principle and Fick's law of diffusion into a computational system to import, calculate, graphically display, and export variables of the Wagner diagram. Main results. The HO2PT models O2 pathway both numerically and graphically according to the Wagner diagram and pertains to conditions under which the mitochondrial oxidative capacity of metabolically active tissues exceeds the capacity of the O2 transport system to deliver O2 to the mitochondria. The tool is based on the Python open source code and libraries and freely and publicly available online for Windows, macOS, and Linux operating systems. Significance. The HO2PT offers a novel functional and demonstrative platform for those interested in examining and its determinants by using the Wagner diagram. It will improve access to and usability of Wagner's and his colleagues' integrated physiological model and thereby benefit users across the wide spectrum of contexts such as scientific research, education, exercise testing, sports coaching, and clinical medicine.
Anna Crispino et al 2024 Physiol. Meas. 45 065001
Objective. Temperature plays a crucial role in influencing the spatiotemporal dynamics of the heart. Electrical instabilities due to specific thermal conditions typically lead to early period-doubling bifurcations and beat-to-beat alternans. These pro-arrhythmic phenomena manifest in voltage and calcium traces, resulting in compromised contractile behaviors. In such intricate scenario, dual optical mapping technique was used to uncover unexplored multi-scale and nonlinear couplings, essential for early detection and understanding of cardiac arrhythmia. Approach. We propose a methodological analysis of synchronized voltage-calcium signals for detecting alternans, restitution curves, and spatiotemporal alternans patterns under different thermal conditions, based on integral features calculation. To validate our approach, we conducted a cross-species investigation involving rabbit and guinea pig epicardial ventricular surfaces and human endocardial tissue under pacing-down protocols. Main results. We show that the proposed integral feature, as the area under the curve, could be an easily applicable indicator that may enhance the predictability of the onset and progression of cardiac alternans. Insights into spatiotemporal correlation analysis of characteristic spatial lengths across different heart species were further provided. Significance. Exploring cross-species thermoelectric features contributes to understanding temperature-dependent proarrhythmic regimes and their implications on coupled spatiotemporal voltage-calcium dynamics. The findings provide preliminary insights and potential strategies for enhancing arrhythmia detection and treatment.
Jiaxing Qiu et al 2024 Physiol. Meas. 45 055025
Objective. Highly comparative time series analysis (HCTSA) is a novel approach involving massive feature extraction using publicly available code from many disciplines. The Prematurity-Related Ventilatory Control (Pre-Vent) observational multicenter prospective study collected bedside monitor data from extremely preterm infants to identify physiologic features that predict respiratory outcomes. Approach. We calculated a subset of 33 HCTSA features on M 10 min windows of oxygen saturation (SPO2) and heart rate (HR) from the Pre-Vent cohort to quantify predictive performance. This subset included representatives previously identified using unsupervised clustering on HCTSA algorithms. We hypothesized that the best HCTSA algorithms would compare favorably to optimal PreVent physiologic predictor IH90_DPE (duration per event of intermittent hypoxemia events below 90%). Main Results. The top HCTSA features were from a cluster of algorithms associated with the autocorrelation of SPO2 time series and identified low frequency patterns of desaturation as high risk. These features had comparable performance to and were highly correlated with IH90_DPE but perhaps measure the physiologic status of an infant in a more robust way that warrants further investigation. The top HR HCTSA features were symbolic transformation measures that had previously been identified as strong predictors of neonatal mortality. HR metrics were only important predictors at early days of life which was likely due to the larger proportion of infants whose outcome was death by any cause. A simple HCTSA model using 3 top features outperformed IH90_DPE at day of life 7 (.778 versus .729) but was essentially equivalent at day of life 28 (.849 versus .850). Significance. These results validated the utility of a representative HCTSA approach but also provides additional evidence supporting IH90_DPE as an optimal predictor of respiratory outcomes.
Nürfet Balkan et al 2024 Physiol. Meas. 45 05TR01
Objective. The physiological, hormonal and biomechanical changes during pregnancy may trigger sleep disordered breathing (SDB) in pregnant women. Pregnancy-related sleep disorders may associate with adverse fetal and maternal outcomes including gestational diabetes, preeclampsia, preterm birth and gestational hypertension. Most of the screening and diagnostic studies that explore SDB during pregnancy were based on questionnaires which are inherently limited in providing definitive conclusions. The current gold standard in diagnostics is overnight polysomnography (PSG) involving the comprehensive measurements of physiological changes during sleep. However, applying the overnight laboratory PSG on pregnant women is not practical due to a number of challenges such as patient inconvenience, unnatural sleep dynamics, and expenses due to highly trained personnel and technology. Parallel to the progress in wearable sensors and portable electronics, home sleep monitoring devices became indispensable tools to record the sleep signals of pregnant women at her own sleep environment. This article reviews the application of portable sleep monitoring devices in pregnancy with particular emphasis on estimating the perinatal outcomes. Approach. The advantages and disadvantages of home based sleep monitoring systems compared to subjective sleep questionnaires and overnight PSG for pregnant women were evaluated. Main Results. An overview on the efficiency of the application of home sleep monitoring in terms of accuracy and specificity were presented for particular fetal and maternal outcomes. Significance. Based on our review, more homogenous and comparable research is needed to produce conclusive results with home based sleep monitoring systems to study the epidemiology of SDB in pregnancy and its impact on maternal and neonatal health.