Computer-aided diagnosis of congestive heart failure using ECG signals – A review

A Prototype Photoplethysmography Electronic Device that Distinguishes Congestive Heart Failure from Healthy Individuals by Applying Natural Time Analysis

“…Previous ECG-based methods for automated computer aided detection (CAD) of HF using various methodologies have achieved encouraging results. The duration of ECG signal recording was variable and could be as short as 2 s [10,[54][55][56][57][58]. Simple clinical prediction rules (CPR) for HF detection performed less well compared to ECG based methods [59].…”

“…Several studies have attempted to find differences of the autonomic nervous cardiac system that controls the electrical cardiac function between patients with HF and healthy (H) subjects using a variety of algorithms of pattern recognition primary based on long term data [9]. Computer aided detection methods for automatic HF diagnosis using ECG signals have been reported in literature [10]. Although these methods still harbor several limitations there is evidence for increased benefit in using nonlinear features for the automated diagnosis of HF with ECG signals [10].…”

“…Computer aided detection methods for automatic HF diagnosis using ECG signals have been reported in literature [10]. Although these methods still harbor several limitations there is evidence for increased benefit in using nonlinear features for the automated diagnosis of HF with ECG signals [10]. However, since physiological time series may be due to a mixed process, stochastic, and deterministic, the use of fractal dimensions in physiological time series has been occasionally criticized (see [11] and references therein; see also [12]).…”

“…The Natural Time Analysis (NTA) has been applied to complex dynamical systems in various scientific fields, such as Geosciences [13][14][15][16], Seismology [13,16,17], Physics [14,18,19], Biology [13,14], and Medicine [13,[20][21][22][23][24] including applications to ECG focused on the distinction of H from Sudden Cardiac Death (SCD) individuals [20,21,23] as well as from HF individuals [20,21,24]. This distinction is based on the entropy S defined in NTA which is a dynamic entropy depending on the sequential order of pulses and fundamentally different [20,23] than other entropies reviewed in [10]. NTA plays an important role in identifying when a complex system approaches a phase change (dynamic phase transition, in which a critical point is defined).…”

Myocardial infarction detection using artificial intelligence and nonlinear features based on synthesis of the standard 12-lead and Frank XYZ leads

“…In the second aspect, ECG or VCG signal based arrhythmia or MI detection techniques including signal processing and artificial intelligence tools have been developed, such as linear [17,18] and nonlinear methods [19,20], Wavelet Transform (WT) [21], Complex Wavelet Transform (CWT) [22,23], Pitch Synchronous Wavelet Transform (PSWT) [24], Discrete Wavelet Transform (DWT) [25], Kalman filtering (KF) [26], Least Mean Squares algorithm (LMS) [27], ensemble learning [28,29], Artificial Neural Networks (ANN) [30], Adaptive Neuro-fuzzy Inference System (ANFIS) [31], support vector machine (SVM) [20], and deep learning [32][33][34][35][36][37][38][39][40][41][42]. For example, Varatharajan et al [43] used linear discriminant analysis (LDA) to reduce the features presented in the ECG signal, which followed with a SVM model with a weighted kernel function for the classification of cardiac arrhythmia.…”