Obstructive sleep apnea syndrome (OSAS) is a common sleep disorder. It has been reported that approximately 40% of patients with moderate or severe OSAS die within the first eight years of disease. In hospitals, OSAS is inspected using polysomnography, which uses a number of sensors. Because of the cumbersome nature of this polysomnography, an initial OSAS screening is usually conducted. In recent years, OSAS screening techniques using Holter electrocardiogram (ECG) have been reported. However, the techniques so far reported cannot perform an OSAS severity assessment. The present study presents a new method to distinguish the obstructive sleep apnea (OSA) and non-OSA epochs at one-second intervals based on the Apnea Hypopnea Index assessment, defined as the duration of continuous apnea. In the proposed method, the time-frequency components of the heart rate variability and three ECG-derived respiration signals calculated by the complex Morlet wavelet transformation are adopted as features. A support vector machine is employed for classification. The proposed method is evaluated using three eight-hour ECG recordings containing OSA episodes from three subjects. As a result, the sensitivity and specificity of classification are found to reach approximately 90%, a level suitable for OSAS screening in clinical settings.
Obstructive sleep apnea syndrome (OSAS), which is caused by repetitive occlusions of the upper airways, is a common sleep disorder. It has been reported that approximately 80% of sleep apnea cases are OSAS. OSAS itself is not necessarily fatal, but is a cause of hypertension, arrhythmia, cardiac arrest, diabetes, and dyslipidemia, which bring further risks of brain infarct or cardiac infarct [
To assess OSAS, a polysomnogram (PSG) is used. This adopts the electroencephalogram (EEG), electrocardiogram (ECG), peripheral capillary oxygen saturation (SpO2), pressure transducers, and nasal cannula to obtain information related to sleep stages, heart rate, and respiratory variation. Because of the cumbersome nature of PSG inspection, a screening examination using fewer sensors is conducted for patients suspected of having OSAS. The most convenient form of this screening is to use only the ECG.
The Holter ECG-based OSAS screening uses features calculated from the heart rate variability (HRV) and ECG-derived respiration (EDR). The HRV analysis is effective for OSAS detection because the heart rate changes rapidly after an episode of obstructive sleep apnea (OSA). In previous research [
Two approaches are generally used: data mining algorithms such as the support vector machine (SVM), AdaBoost, or linear discriminant analysis [
In previous research using Holter ECG-based OSAS screening, the goal has been to assess the existence or absence of an OSA episode. In other words, existing OSAS screening methods cannot determine the severity of OSAS. Generally, OSAS severity is determined using the Apnea Hypopnea Index (AHI), whereby the severity of OSAS is defined by the total number of apnea and hypopnea per hour of sleep [
In the AHI-based assessment, the duration of an OSA episode must be measured. However, no previous studies record the duration of OSA episodes. In most studies, the existence or absence of an OSA episode is evaluated at one-minute intervals, as in the PhysioNet/CinC challenge (in the PhysioNet database, the existence or non-existence of OSA episodes is annotated every minute).
In this study, we propose a method that detects OSA episodes at one-second intervals to quantitatively assess the OSAS severity. For this purpose, the time-frequency components of EDR and HRV signals are adopted as features of OSA and non-OSA, meaning that relatively few beats are needed. Our motivation is simply to realize a practical and quantitative method for OSAS severity screening, and to contribute to the early detection and treatment of OSAS.
In this paper, the time-frequency components of the EDR and HRV computed with the complex Morlet wavelet transformation (CMORWT) are used as classification features for OSA and non-OSA epochs, and an SVM is used as the classifier. The process of the overall methodology is shown in
In previous studies [
Severity | AHI |
---|---|
None/minimal | <5 per hour |
Mild | ≥5, but <15 per hour |
Moderate | ≥15, but <30 per hour |
Severe | ≥30 per hour |
600 ms before future extractions, because the baseline wander decreases the accuracy of the EDR series. After median filtering, R wave detection can be performed using the wavelet transformation modulus maxima (WTMM) method [
On the basis of previous research regarding Holter ECG-based OSAS screening, we adopt the HRV and EDR signals calculated from the area of the R wave in this research. In addition, two further EDR methods are proposed in this research.
・ HRV
HRV is the cycle variation over the duration of the heartbeat, which is defined as the time interval from one R wave to the next. This is also called the R-R interval time series, and is computed as follows:
where
・ EDR1: Area of R wave
For the first EDR, an area of the R wave is computed [
・ EDR2: QRST integration
The second EDR method involves QRST integration, as shown in
respiration. In fact, it may be that the QRST integration is more sensitive to changes in respiration than the area of the R wave. In this study, the onset and offset of each elementary wave (i.e., Q and T waves) is also detected by the WTMM method [
・ EDR3: Area between two R waves
Although EDR1 and EDR2 are promising for OSAS detection, they have the practical disadvantage that the onset or offset of each elementary wave cannot necessarily be detected in a Holter ECG recording. The detection of an R wave is more robust than for other elementary waves. Therefore, we propose the area between two R waves (
Next, the time-frequency components of the calculated EDR and HRV signals are computed using the wavelet transformation. In this research, we adopt the CMORWT, because it can relate scale levels to actual frequencies [
The mother wavelet of CMORWT is defined as:
where
Let S(t) denote the EDR or HRV signal obtained from the ECG. Its wavelet component
for f < {0.01, 0.02, …, 0.4} Hz.
In this research, we adopt an SVM [
In previous research [
The input data for the SVM classifier are the time-frequency components obtained by CMORWT at one- second intervals. We randomly select 50% of the measured ECG signals as learning data, and use the remaining data for the evaluation.
To evaluate the proposed method, three subjects’ ECG recordings during sleep were used. All subjects were male, aged from 23 - 50. They may be OSAS candidates. The ECG signals were measured using a mobile OSAS monitor, the SAS-3200 produced by Nihon Kohden. ECG electrodes were placed on CM5, NASA, and CC5. (In this paper, CM5, NASA, and CC5 are called ch1, ch2, and ch3, respectively.) The ECG sampling rate was 1000 Hz. The recording length for each subject was approximately eight hours. Simultaneously, the actual respiration signals were measured using an air flow sensor with sampling rates of 40 Hz.
We labeled the measured ECG signal as an “OSA epoch” or “non-OSA epoch” according to respiratory waveform measured with the air flow sensor. In particular, when air flow was absent or decreased, its epoch was labeled as “OSA epoch”. Other epochs were labeled as “non-OSAS epoch”. These labeled epochs were used in the learning and evaluation steps.
To validate the effectiveness of the proposed method, the classification accuracy for OSA and non-OSA epochs was evaluated in each ECG channel. As evaluation indices for classification accuracy, we used the sensitivity (=TP/TP + FN) and specificity (=TP/TP + FP), where TP, TN, FN, and FP denote the true number of OSA epochs, true number of non-OSA epochs, false negative, and false positives, respectively.
Figures 3-6 show the EDR1, EDR2, EDR3, and HRV spectrograms for one subject. These EDR spectrograms were obtained from the ECG signal measured through channel 3. The figures show the time-frequency components of OSA and non-OSA epochs. The colors in Figures 3-6 represent the energy intensity, with red indicating higher energy and blue indicating lower energy. From these figures, it can be seen that the time-frequency feature of the HRV series is most effective for the classification of OSA and non-OSA epochs. In particular, it can be seen that the energy difference between OSA and non-OSA epochs was large in the low-frequency region (0.01 - 0.05 Hz). Similarly, the effectiveness of the low-frequency region can be seen in the three EDR spectrograms. Among these, EDR3, which is calculated from the area between two R waves, seems to be more efficient than the other EDR methods.
Next, the distributions of low-frequency components of OSA and non-OSA epochs are presented to further confirm the effectiveness of EDR3 and HRV’s in this region.
of HRV and EDR3. The frequency range was from 0.01 - 0.05 Hz.
In contrast, although Figures 3-7 suggest that low-frequency components are effective,
Channel | Sensitivity (%) | Specificity (%) |
---|---|---|
1 | 67 | 83 |
2 | 31 | 53 |
3 | 82 | 100 |
feature selection method will increase the classification accuracy.
From these results, we can conclude that the classification of OSA and non-OSA epochs at one-second intervals is feasible for AHI-based assessment with time-frequency components of HRV and those of EDRs
Channel | Sensitivity (%) | Specificity (%) |
---|---|---|
1 | 71 | 76 |
2 | 29 | 5 |
3 | 85 | 99 |
computed from a suitable channel. However, the number of subjects in this study was not significant. In future work, we will conduct experiments with a greater number of subjects to evaluate the validity of the proposed method.
This study proposed a new method to evaluate the AHI for OSAS screening using Holter ECG. The proposed method distinguishes OSA and non-OSA epochs every second. The time-frequency components of HRV and three EDR signals computed by CMORWT were used as features, and, classification was performed with SVM. Three eight-hour ECG recordings containing OSA episodes from three subjects were used to evaluate the proposed method. Our results showed that the sensitivity and specificity of classification reached approximately 90%, indicating the feasibility of this approach for clinical OSAS screening to enable early detection and treatment.
MotokiSakai,DamingWei, (2015) Holter ECG-Based Apnea Hypopnea Index to Screen Obstructive Sleep Apnea: A New Proposal and Evaluation of Feasibility. Journal of Biosciences and Medicines,03,33-41. doi: 10.4236/jbm.2015.311004