Modulation spectrum analysis for noisyelectrocardiogram signal processing and applications.

Advances in wearable electrocardiogram (ECG) monitoring devices have allowed for new cardiovascular applications to emerge beyond diagnostics, such as stress and fatigue detection, athletic performance assessment, sleep disorder characterization, mood recognition, activity surveillance, biometrics, and fitness tracking, to name a few. Such devices, however, are prone to artifacts, particularly due to movement, thus hampering heart rate and heart rate variability measurement and posing a serious threat to cardiac monitoring applications. To address these issues, this thesis proposes the use of a spectro-temporal signal representation called “modulation spectrum”, which is shown to accurately separate cardiac and noise components from the ECG signals, thus opening doors for noise-robust ECG signal processing tools and applications. First, an innovative ECG quality index based on the modulation spectral signal representation is proposed. The representation quantifies the rate-of-change of ECG spectral components, which are shown to be different from the rate-of-change of typical ECG noise sources. As such, a signal-to-noise ratio (SNR) like metric is proposed, termed modulation spectral based quality index (MS-QI). Unlike existing quality metrics, MS-QI does not rely on machine learning algorithms, can be performed on single-lead ECGs, and was shown to perform accurately with synthetic ECGs, as well as ECGs recorded in real-world environments. Based on insights obtained from the MS-QI metric, a new adaptive ECG enhancement algorithm is then proposed based on the principle of bandpass filtering in the modulation spectral domain. The algorithm was tested on synthetic and recorded (extremely noisy) ECG databases. Experimental results show the proposed algorithm outperforming a stateof- the-art wavelet-based enhancement algorithm in terms of heart rate (HR) error percentage measurement, signal-to-noise ratio (SNR) improvement, and ECG kurtosis; the latter is a widely-used ECG quality metric. These findings suggest that the proposed algorithm can be used to enhance the quality of wearable ECG monitors even in extreme conditions, thus it can play a key role in athletic peak performance training/monitoring. Moreover, wearable ECG monitoring applications are burgeoning and typically rely on estimates of heart rate variability (HRV). Such applications require small computational footprint and cannot rely on enhancement and HRV analysis, thus a stand-alone HRV metric is needed. HRV indices have been proposed based on time- and frequency-domain analyses of the ECG, as well as via non-linear approaches. These methods, however, are very sensitive to ECG artefacts, thus limiting the number of applications involving noisy ECGs (e.g., athletic peak performance training). Typically, ECG enhancement is performed prior to HRV computation to overcome this limitation. Existing enhancement algorithms, however, are not accurate in very noisy scenarios. Hence, an alternate approach is proposed based on the modulation spectrum. By quantifying the rate-of-change of ECG spectral components over time, we show that heart rate estimates can be reliably obtained even in extremely noisy signals, thus bypassing the need for ECG enhancement. The so-called MD-HRV (modulation domain HRV) is tested on synthetic and recorded noisy ECG signals and shown to outperform several benchmark HRV metrics computed post-enhancement. These findings suggest that the proposed MD-HRV metric is well-suited for ambulant cardiac monitoring applications, particularly those involving intense movement. Finally, a quality-aware ECG monitoring application is presented based on the proposed MS-QI. Wearable ECG devices are increasingly being used in telehealth applications, particularly for patient monitoring applications. Representative devices include watches, chest straps, and even smart clothing via textile ECG sensors. Such lower-cost sensors, however, are extremely sensitive to movement, thus pose a serious threat to such ECG streaming applications. For example, transmission bandwidth, battery life, and/or storage space can be spent with ECG segments that convey little cardiac information due to the high levels of noise present. Moreover, noisy signals may cause false alarms in automated patient monitoring systems, thus increasing the burden on medical personnel. Here, by employing the MS-QI to discriminate usable from non-usable ECG segments, a quality-aware storage protocol was implemented where storage of cardiac parameters was only performed on the usable segments. When tested with a smart shirt under three conditions, namely sitting, walking and running, the proposed quality-aware application resulted in storage savings of 65%.