Direct application of an ECG-based sleep staging algorithm on reflective photoplethysmography data decreases performance

Abstract Objective The maturation of neural network-based techniques in combination with the availability of large sleep datasets has increased the interest in alternative methods of sleep monitoring. For unobtrusive sleep staging, the most promising algorithms are based on heart rate variability computed from inter-beat intervals (IBIs) derived from ECG-data. The practical application of these algorithms is even more promising when alternative ways of obtaining IBIs, such as wrist-worn photoplethysmography (PPG) can be used. However, studies validating sleep staging algorithms directly on PPG-based data are limited. Results We applied an automatic sleep staging algorithm trained and validated on ECG-data directly on inter-beat intervals derived from a wrist-worn PPG sensor, in 389 polysomnographic recordings of patients with a variety of sleep disorders. While the algorithm reached moderate agreement with gold standard polysomnography, the performance was significantly lower when applied on PPG- versus ECG-derived heart rate variability data (kappa 0.56 versus 0.60, p < 0.001; accuracy 73.0% versus 75.9% p < 0.001). These results show that direct application of an algorithm on a different source of data may negatively affect performance. Algorithms need to be validated using each data source and re-training should be considered whenever possible.

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Application of Mathematical Methods for Analysis of Digital ECG Data

Information Technologies and Control ◽

10.1515/itc-2017-0005 ◽

2016 ◽

Vol 14 (2) ◽

pp. 35-44

Author(s):

G. Georgieva-Tsaneva

Keyword(s):

Heart Rate ◽

Heart Rate Variability ◽

Diagnostic Technique ◽

Digital Data ◽

Mathematical Methods ◽

Nonlinear Methods ◽

Non Invasive ◽

Cardiac Autonomic Regulation ◽

Variability Data ◽

Ecg Data

AbstractThis paper presents several mathematical methods for analysis of electrocardiogram digital data. The measurement of beat to beat fluctuations known as Heart Rate Variability becomes a non-invasive diagnostic technique to study the cardiac autonomic regulation. The analysis was done by software developed by the author. The article presents the results of linear methods, nonlinear methods and wavelet analysis of Heart Rate Variability data in healthy and diseased subjects. The obtained results and the performed comparative analysis demonstrate the possibility for effective application of the considered methods in new cardiovascular information systems.

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Emotion Recognition from Heart Rate Variability Data of Smartwatch While Watching a Video

10.23919/icmu50196.2021.9638844 ◽

2021 ◽

Author(s):

Reika Takeshita ◽

Aya Shoji ◽

Tahera Hossain ◽

Anna Yokokubo ◽

Guillaume Lopez

Keyword(s):

Heart Rate ◽

Heart Rate Variability ◽

Emotion Recognition ◽

Variability Data

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Typoloagical characteristics of the functional state of regulatory systems in schoolchildren and young athletes (According to Heart Rate Variability Data)

Human Physiology ◽

10.1134/s0362119709060103 ◽

2009 ◽

Vol 35 (6) ◽

pp. 730-738 ◽

Cited By ~ 1

Author(s):

N. I. Shlyk ◽

E. N. Sapozhnikova ◽

T. G. Kirillova ◽

V. G. Semenov

Keyword(s):

Heart Rate ◽

Heart Rate Variability ◽

Functional State ◽

Young Athletes ◽

Regulatory Systems ◽

Variability Data

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Behavioral and Physiological Differences between Working Horses and Chilean Rodeo Horses in a Handling Test

Animals ◽

10.3390/ani9070397 ◽

2019 ◽

Vol 9 (7) ◽

pp. 397 ◽

Cited By ~ 4

Author(s):

Paula Rosselot ◽

Tiago Mendonça ◽

Igor González ◽

Tamara Tadich

Keyword(s):

Heart Rate ◽

Heart Rate Variability ◽

Physiological Responses ◽

Group Differences ◽

Monitor System ◽

The Novel ◽

Non Invasive ◽

Lower Variability ◽

Variability Data ◽

Selection Of

Non-invasive measures are preferred when assessing animal welfare. Differences in behavioral and physiological responses toward a stressor could be the result of the selection of horses for specific uses. Behavioral and physiological responses of working and Chilean rodeo horses subjected to a handling test were assessed. Five behaviors, number of attempts, and the time to cross a bridge were video recorded and analyzed with the Observer XT software. Heart rate (HR) and heart rate variability (HRV), to assess the physiological response to the novel stimulus, were registered with a Polar Equine V800 heart rate monitor system during rest and the bridge test. Heart rate variability data were obtained with the Kubios software. Differences between working and Chilean rodeo horses were assessed, and within-group differences between rest and the test were also analyzed. Chilean rodeo horses presented more proactive behaviors and required significantly more attempts to cross the bridge than working horses. Physiologically, Chilean rodeo horses presented lower variability of the heart rate than working horses.

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Automatic sleep staging using heart rate variability, body movements, and recurrent neural networks in a sleep disordered population

SLEEP ◽

10.1093/sleep/zsaa048 ◽

2020 ◽

Vol 43 (9) ◽

Cited By ~ 1

Author(s):

Pedro Fonseca ◽

Merel M van Gilst ◽

Mustafa Radha ◽

Marco Ross ◽

Arnaud Moreau ◽

...

Keyword(s):

Neural Networks ◽

Heart Rate ◽

Heart Rate Variability ◽

Sleep Disorders ◽

Recurrent Neural Networks ◽

Clinical Diagnostics ◽

Sleep Staging ◽

Substantial Agreement ◽

Body Movements ◽

Wide Range

Abstract Study Objectives To validate a previously developed sleep staging algorithm using heart rate variability (HRV) and body movements in an independent broad cohort of unselected sleep disordered patients. Methods We applied a previously designed algorithm for automatic sleep staging using long short-term memory recurrent neural networks to model sleep architecture. The classifier uses 132 HRV features computed from electrocardiography and activity counts from accelerometry. We retrained our algorithm using two public datasets containing both healthy sleepers and sleep disordered patients. We then tested the performance of the algorithm on an independent hold-out validation set of sleep recordings from a wide range of sleep disorders collected in a tertiary sleep medicine center. Results The classifier achieved substantial agreement on four-class sleep staging (wake/N1–N2/N3/rapid eye movement [REM]), with an average κ of 0.60 and accuracy of 75.9%. The performance of the sleep staging algorithm was significantly higher in insomnia patients (κ = 0.62, accuracy = 77.3%). Only in REM parasomnias, the performance was significantly lower (κ = 0.47, accuracy = 70.5%). For two-class wake/sleep classification, the classifier achieved a κ of 0.65, with a sensitivity (to wake) of 72.9% and specificity of 94.0%. Conclusions This study shows that the combination of HRV, body movements, and a state-of-the-art deep neural network can reach substantial agreement in automatic sleep staging compared with polysomnography, even in patients suffering from a multitude of sleep disorders. The physiological signals required can be obtained in various ways, including non-obtrusive wrist-worn sensors, opening up new avenues for clinical diagnostics.

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Experimental Study for Determining the Parameters Required for Detecting ECG and EEG Related Diseases during the Timed-Up and Go Test

Computers ◽

10.3390/computers9030067 ◽

2020 ◽

Vol 9 (3) ◽

pp. 67

Author(s):

Vasco Ponciano ◽

Ivan Miguel Pires ◽

Fernando Reinaldo Ribeiro ◽

María Vanessa Villasana ◽

Maria Canavarro Teixeira ◽

...

Keyword(s):

Heart Rate ◽

Experimental Study ◽

Heart Rate Variability ◽

Mobile Devices ◽

Brain Activity ◽

Health Conditions ◽

Qrs Complex ◽

Timed Up And Go ◽

Eeg Data ◽

Ecg Data

The use of smartphones, coupled with different sensors, makes it an attractive solution for measuring different physical and physiological features, allowing for the monitoring of various parameters and even identifying some diseases. The BITalino device allows the use of different sensors, including Electroencephalography (EEG) and Electrocardiography (ECG) sensors, to study different health parameters. With these devices, the acquisition of signals is straightforward, and it is possible to connect them using a Bluetooth connection. With the acquired data, it is possible to measure parameters such as calculating the QRS complex and its variation with ECG data to control the individual’s heartbeat. Similarly, by using the EEG sensor, one could analyze the individual’s brain activity and frequency. The purpose of this paper is to present a method for recognition of the diseases related to ECG and EEG data, with sensors available in off-the-shelf mobile devices and sensors connected to a BITalino device. The data were collected during the elderly’s experiences, performing the Timed-Up and Go test, and the different diseases found in the sample in the study. The data were analyzed, and the following features were extracted from the ECG, including heart rate, linear heart rate variability, the average QRS interval, the average R-R interval, and the average R-S interval, and the EEG, including frequency and variability. Finally, the diseases are correlated with different parameters, proving that there are relations between the individuals and the different health conditions.

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Comparison of Heart Rate Recovery Following Exercise to Device-Based Heart Rate Variability Data

Journal of Cardiac Failure ◽

10.1016/j.cardfail.2006.06.226 ◽

2006 ◽

Vol 12 (6) ◽

pp. S66

Author(s):

Michael P. Husby ◽

Steve Eddy ◽

Satish Goel ◽

David B. De Lurgio

Keyword(s):

Heart Rate ◽

Heart Rate Variability ◽

Heart Rate Recovery ◽

Variability Data

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Anova-Based Method for Hurst Exponent Estimation of Heart Rate Variability Sequences

Information Technologies and Control ◽

10.1515/itc-2015-0006 ◽

2014 ◽

Vol 12 (1) ◽

pp. 9-14

Author(s):

G. Georgieva-Tsaneva ◽

M. Dimitrova

Keyword(s):

Heart Rate ◽

Heart Rate Variability ◽

Information Systems ◽

Comparative Analysis ◽

Hurst Exponent ◽

Medical Information ◽

Medical Data ◽

Medical Information Systems ◽

Variability Data

Abstract A method for determination of the Hurst exponent based on Analysis of Variance for processing of medical data sequences is proposed in the paper. It is compared to the “rescaled adjusted range method” developed by Hurst and applied in this paper to heart rate variability data. The obtained results and the performed comparative analysis demonstrate the possibility for effective application of the proposed method in novel medical information systems.

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