Med Biol Eng Comput DOI 10.1007/s11517-015-1249-z
ORIGINAL ARTICLE
The addition of entropy‑based regularity parameters improves sleep stage classification based on heart rate variability M. Aktaruzzaman · M. Migliorini · M. Tenhunen · S. L. Himanen · A. M. Bianchi · R. Sassi
Received: 24 July 2014 / Accepted: 2 February 2015 © International Federation for Medical and Biological Engineering 2015
Abstract The work considers automatic sleep stage classification, based on heart rate variability (HRV) analysis, with a focus on the distinction of wakefulness (WAKE) from sleep and rapid eye movement (REM) from non-REM (NREM) sleep. A set of 20 automatically annotated one-night polysomnographic recordings was considered, and artificial neural networks were selected for classification. For each inter-heartbeat (RR) series, beside features previously presented in literature, we introduced a set of four parameters related to signal regularity. RR series of three different lengths were considered (corresponding to 2, 6, and 10 successive epochs, 30 s each, in the same sleep stage). Two sets of only four features captured 99 % of the data variance in each classification problem, and both of them contained one of the new regularity features proposed. The accuracy of classification for REM versus NREM (68.4 %, 2 epochs; 83.8 %, 10 epochs) was higher than when distinguishing WAKE versus SLEEP (67.6 %, 2 epochs; 71.3 %, 10 epochs). Also, the reliability parameter (Cohens’s Kappa) was higher (0.68 and 0.45, respectively). Sleep staging classification based on HRV was still less precise than other staging methods, employing a larger variety of signals collected during polysomnographic studies. However, cheap and unobtrusive HRV-only sleep classification proved sufficiently precise for a wide range of applications. M. Aktaruzzaman · R. Sassi Dipartimento di Informatica, Università degli Studi di Milano, Crema, Italy M. Migliorini (*) · A. M. Bianchi Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Via Golgi 39, 20133 Milano, Italy e-mail: [email protected] M. Tenhunen · S. L. Himanen Department of Clinical Neurophysiology, Pirkanmaa Hospital District, Tampere, Finland
Keywords Automatic sleep classification · Heart rate variability · Sample entropy · RR series · Regularity
1 Introduction Sleep has a significant effect on many physiological functions and plays a fundamental role in the genesis and insurgence of different pathologies (cardiologic, neurological, and metabolic). Its quality is one of the aspects that mostly influence our everyday life. It has a strong impact on natural processes like memorization, learning, and concentration [29]. Poor sleep quality or too short sleep time have been identified among the main causes of car or work accidents [24]. Also, sleep disturbances (i.e., the ones related to breathing) have a strong association with cardiovascular pathologies. A bad quality of sleep has an impact on blood pressure, decreases the immunity defenses, and may increase the insurgence probability of metabolic disturbances such as obesity and diabetes [7, 9, 17, 34]. Sleep quality is generally evaluated through polysomnography (PSG), which consists of many physiological signals recorded during one or more nights of sleep: electroencephalogram (EEG), electro-myogram (EMG), and electro-oculogram (EOG), besides respiration activity and electrocardiogram (ECG). Rules and guidelines provided by the American Academy of Sleep Medicine (AASM) allow the evaluation of wakefulness, sleep macrostructures, built through the alternation of different sleep stages as rapid eye movement (or REM), non-REM light sleep (stage 1 and 2), non-REM (or NREM) deep sleep (stage 3, also called slow-wave sleep, SWS), and microstructures, such as the cyclic alternating pattern (CAP sleep), K-complexes, microarousals [13, 16, 30].
13
Med Biol Eng Comput
The standard practice is to perform sleep evaluation by the visual or semi-automatic scoring of polysomnographic traces [3]. This technique requires specific instrumentation and signals which are recorded and scored by trained personnel. In addition, their acquisition may be so comfortless to affect the sleep quality itself. On the other hand, many different studies have demonstrated that sleep strongly affects the peripheral system, particularly the autonomic nervous system, so that the heart rate variability (HRV) signal presents different patterns during different sleep stages [14, 20, 23, 27] and during sleep phasic events [12, 28]. For these reasons, many recent studies have focused on the effects of sleep stage transitions on peripheral systems. Most of the works found in the literature have given emphasis on the correspondence of different pattern of heart rate with different sleep stages [35], and more recently, a few works [8, 33] described methods to perform sleep staging through HRV analysis. One of the advantages of using HRV for sleep evaluation is the possibility of employing less intrusive devices, such as a sensorized T-shirt or mattress [4, 15, 21]. Different features have shown to be promising in terms of differentiation between different sleep stages, using HRV analysis, e.g., features in both time and frequency domains (mean and standard deviation of HRV; total power (TP); very low frequency power (VLF); low-frequency power (LF), and high-frequency power (HF) [20, 25, 31]. In addition, approximate entropy (ApEn) and sample entropy (SampEn), two commonly used tools for nonlinear dynamic analysis of HRV, were considered. Estrada et al. [10] employed ApEn, as well as time and frequency domain features, of EEG and, then separately, of HRV. They found that features extracted during REM sleep, both from EEG and HRV, always overlapped with the features of any other stage. The use of SampEn for characterizing sleep stages from HRV was first demonstrated by Vigo et al. [32]. They analyzed 5-min epochs of wake state, stage 3 of NREM, and REM sleep. They found that SampEn in REM and deep sleep was significantly different (p
ORIGINAL ARTICLE
The addition of entropy‑based regularity parameters improves sleep stage classification based on heart rate variability M. Aktaruzzaman · M. Migliorini · M. Tenhunen · S. L. Himanen · A. M. Bianchi · R. Sassi
Received: 24 July 2014 / Accepted: 2 February 2015 © International Federation for Medical and Biological Engineering 2015
Abstract The work considers automatic sleep stage classification, based on heart rate variability (HRV) analysis, with a focus on the distinction of wakefulness (WAKE) from sleep and rapid eye movement (REM) from non-REM (NREM) sleep. A set of 20 automatically annotated one-night polysomnographic recordings was considered, and artificial neural networks were selected for classification. For each inter-heartbeat (RR) series, beside features previously presented in literature, we introduced a set of four parameters related to signal regularity. RR series of three different lengths were considered (corresponding to 2, 6, and 10 successive epochs, 30 s each, in the same sleep stage). Two sets of only four features captured 99 % of the data variance in each classification problem, and both of them contained one of the new regularity features proposed. The accuracy of classification for REM versus NREM (68.4 %, 2 epochs; 83.8 %, 10 epochs) was higher than when distinguishing WAKE versus SLEEP (67.6 %, 2 epochs; 71.3 %, 10 epochs). Also, the reliability parameter (Cohens’s Kappa) was higher (0.68 and 0.45, respectively). Sleep staging classification based on HRV was still less precise than other staging methods, employing a larger variety of signals collected during polysomnographic studies. However, cheap and unobtrusive HRV-only sleep classification proved sufficiently precise for a wide range of applications. M. Aktaruzzaman · R. Sassi Dipartimento di Informatica, Università degli Studi di Milano, Crema, Italy M. Migliorini (*) · A. M. Bianchi Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Via Golgi 39, 20133 Milano, Italy e-mail: [email protected] M. Tenhunen · S. L. Himanen Department of Clinical Neurophysiology, Pirkanmaa Hospital District, Tampere, Finland
Keywords Automatic sleep classification · Heart rate variability · Sample entropy · RR series · Regularity
1 Introduction Sleep has a significant effect on many physiological functions and plays a fundamental role in the genesis and insurgence of different pathologies (cardiologic, neurological, and metabolic). Its quality is one of the aspects that mostly influence our everyday life. It has a strong impact on natural processes like memorization, learning, and concentration [29]. Poor sleep quality or too short sleep time have been identified among the main causes of car or work accidents [24]. Also, sleep disturbances (i.e., the ones related to breathing) have a strong association with cardiovascular pathologies. A bad quality of sleep has an impact on blood pressure, decreases the immunity defenses, and may increase the insurgence probability of metabolic disturbances such as obesity and diabetes [7, 9, 17, 34]. Sleep quality is generally evaluated through polysomnography (PSG), which consists of many physiological signals recorded during one or more nights of sleep: electroencephalogram (EEG), electro-myogram (EMG), and electro-oculogram (EOG), besides respiration activity and electrocardiogram (ECG). Rules and guidelines provided by the American Academy of Sleep Medicine (AASM) allow the evaluation of wakefulness, sleep macrostructures, built through the alternation of different sleep stages as rapid eye movement (or REM), non-REM light sleep (stage 1 and 2), non-REM (or NREM) deep sleep (stage 3, also called slow-wave sleep, SWS), and microstructures, such as the cyclic alternating pattern (CAP sleep), K-complexes, microarousals [13, 16, 30].
13
Med Biol Eng Comput
The standard practice is to perform sleep evaluation by the visual or semi-automatic scoring of polysomnographic traces [3]. This technique requires specific instrumentation and signals which are recorded and scored by trained personnel. In addition, their acquisition may be so comfortless to affect the sleep quality itself. On the other hand, many different studies have demonstrated that sleep strongly affects the peripheral system, particularly the autonomic nervous system, so that the heart rate variability (HRV) signal presents different patterns during different sleep stages [14, 20, 23, 27] and during sleep phasic events [12, 28]. For these reasons, many recent studies have focused on the effects of sleep stage transitions on peripheral systems. Most of the works found in the literature have given emphasis on the correspondence of different pattern of heart rate with different sleep stages [35], and more recently, a few works [8, 33] described methods to perform sleep staging through HRV analysis. One of the advantages of using HRV for sleep evaluation is the possibility of employing less intrusive devices, such as a sensorized T-shirt or mattress [4, 15, 21]. Different features have shown to be promising in terms of differentiation between different sleep stages, using HRV analysis, e.g., features in both time and frequency domains (mean and standard deviation of HRV; total power (TP); very low frequency power (VLF); low-frequency power (LF), and high-frequency power (HF) [20, 25, 31]. In addition, approximate entropy (ApEn) and sample entropy (SampEn), two commonly used tools for nonlinear dynamic analysis of HRV, were considered. Estrada et al. [10] employed ApEn, as well as time and frequency domain features, of EEG and, then separately, of HRV. They found that features extracted during REM sleep, both from EEG and HRV, always overlapped with the features of any other stage. The use of SampEn for characterizing sleep stages from HRV was first demonstrated by Vigo et al. [32]. They analyzed 5-min epochs of wake state, stage 3 of NREM, and REM sleep. They found that SampEn in REM and deep sleep was significantly different (p
