Unsupervised detection and classification of heartbeats using the dissimilarity matrix in PCG signals

Torre-Cruz, J.; Martinez-Munoz, D.; Ruiz-Reyes, N.; Muñoz-Montoro, Antonio J.; Puentes-Chiachio, M; Canadas-Quesada, F.

doi:10.1016/j.cmpb.2022.106909

Cited by 11 publications

(4 citation statements)

References 39 publications

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“…Next, we further refine the signal by implementing a band-pass filter. This filter restricts the frequency range of the spectrogram to a band spanning 300 to 2000 Hz, a spectral interval where most of the respiratory content can be found [36,37], given that cardiac and other body sounds lay under 300 Hz and, yet, most of the lung noises can be foundabove this threshold.…”

Section: Signal Preprocessingmentioning

confidence: 99%

Noise-tolerant NMF-based parallel algorithm for respiratory rate estimation

Revuelta-Sanz,

Muñoz-Montoro,

Torre-Cruz

et al. 2023

Preprint

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The accurate estimation of respiratory rate (RR) is crucial for assessing the respiratory system's health in humans, particularly during auscultation processes. Despite numerous automated RR estimation approaches proposed in the literature, challenges persist in accurately estimating RR in noisy environments, typical of real-life situations. This becomes especially critical when periodic noise patterns interfere with the target signal. In this study, we present a parallel driver designed to address the challenges of RR estimation in real-world environments, combining multi-core architectures with parallel and high-performance techniques. The proposed system employs a non-negative matrix factorization (NMF) approach to mitigate the impact of noise interference in the input signal. This NMF approach is guided by pre-trained bases of respiratory sounds and incorporates an orthogonal constraint to enhance accuracy. The proposed solution is tailored for real-time processing on low-power hardware. Experimental results across various scenarios demonstrate promising outcomes in terms of accuracy and computational efficiency.

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Section: Signal Preprocessingmentioning

confidence: 99%

Noise-tolerant NMF-based parallel algorithm for respiratory rate estimation

Revuelta-Sanz,

Muñoz-Montoro,

Torre-Cruz

et al. 2023

Preprint

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“…Because stethoscopes are sensitive to environmental noises and other sounds from the human body (e.g., respiratory sounds, lung sounds, rumbling of the stomach and intestine), denoising is strongly required to improve the accuracy of heart sounds localization [ 10 , 17 , 18 , 19 , 20 , 21 ]. Several techniques, such as short-time Fourier transform, fast Wavelet transform, tunable-Q Wavelet transform, and S transform, have been used to accomplish this task [ 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 ]. Different approaches have been proposed in the literature for heart sounds localization.…”

Section: Introductionmentioning

confidence: 99%

“…However, the sole thresholding operation often fails to selectively detect peaks related to heart sounds. Nevertheless, the great part of the envelogram-based methods is unable to discriminate S1 and S2 [ 23 , 26 , 29 , 32 ], or they need to consider additional criteria such as the typical duration of systole and diastole, to distinguish them [ 22 , 24 , 28 , 31 , 33 , 34 ].…”

Section: Introductionmentioning

confidence: 99%

Accurate Localization of First and Second Heart Sounds via Template Matching in Forcecardiography Signals

Centracchio,

Parlato,

Esposito

et al. 2024

Sensors

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Cardiac auscultation is an essential part of physical examination and plays a key role in the early diagnosis of many cardiovascular diseases. The analysis of phonocardiography (PCG) recordings is generally based on the recognition of the main heart sounds, i.e., S1 and S2, which is not a trivial task. This study proposes a method for an accurate recognition and localization of heart sounds in Forcecardiography (FCG) recordings. FCG is a novel technique able to measure subsonic vibrations and sounds via small force sensors placed onto a subject’s thorax, allowing continuous cardio-respiratory monitoring. In this study, a template-matching technique based on normalized cross-correlation was used to automatically recognize heart sounds in FCG signals recorded from six healthy subjects at rest. Distinct templates were manually selected from each FCG recording and used to separately localize S1 and S2 sounds, as well as S1–S2 pairs. A simultaneously recorded electrocardiography (ECG) trace was used for performance evaluation. The results show that the template matching approach proved capable of separately classifying S1 and S2 sounds in more than 96% of all heartbeats. Linear regression, correlation, and Bland–Altman analyses showed that inter-beat intervals were estimated with high accuracy. Indeed, the estimation error was confined within 10 ms, with negligible impact on heart rate estimation. Heart rate variability (HRV) indices were also computed and turned out to be almost comparable with those obtained from ECG. The preliminary yet encouraging results of this study suggest that the template matching approach based on normalized cross-correlation allows very accurate heart sounds localization and inter-beat intervals estimation.

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“…Given the convenience and feasibility of using a stethoscope to acquire heart rate data, PCG signals have been employed as raw data input to the classification model in the proposed work for detecting heart health status. In the literature, both supervised (Das et al, 2019; Moulana et al, 2022) and unsupervised (Das et al, 2020; Torre‐Cruz et al, 2022) approaches of heart health status detection have been reported. The unsupervised methods, despite their potential, have some limitations about their usability and accuracy (Safara et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

Classifying the heart sound signals using textural‐based features for an efficient decision support system

2023

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Cardiovascular diseases have surpassed cancer as the leading cause of death on the planet today. Numerous decision‐making systems with computer‐assisted support have been developed to assist cardiologists to detect heart disease, and thus, lowering the mortality rate. The purpose of this research is to classify audio signals received from the heart as normal or abnormal. The PhysioNet Computing in Cardiology (CinC) 2016 benchmark dataset, popularly known as PhysioNet 2016, has been used to validate the proposed methodology presented here. PhysioNet 2016 contains a total of 3200 phonocardiogram (PCG) recordings divided into sub‐datasets A‐F. The state‐of‐the‐art studies conducted till date have not considered the harmonic details of the beat that can be extracted from its equivalent chromagram image. In this work, textural features such as linear binary pattern (LBP), adaptive‐LBP, and ring‐LBP have been extracted from the existing spectrogram and combined with the features extracted from the chromagram. It has been observed that the combination of features extracted from both the image variants has resulted in a greater accuracy as compared to the scenario where researchers were using only the spectrogram. The experiment yielded the mean accuracy, precision, and F1‐score as 94.87, 93.11, and 95.273, respectively. The heart sound classification models employ spectrogram, scalogram, and mel‐spectrogram images to view and analyse the acoustic properties of a PCG signal. Although these visual tools provide useful information about the signal, yet they are unable to distinguish between pitch and resonance in heart sound generation. However, this paper proposes an alternative approach of heart sound signal representation that allows for a more precise measure of pitch‐related changes in the heart sound. Its results highlight the significance of extracting textural features from time‐chroma representation (i.e., chromagram) of PCG signals that have not been explored yet in the domain related to classification of heart sound signals as normal and abnormal.

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Unsupervised detection and classification of heartbeats using the dissimilarity matrix in PCG signals

Cited by 11 publications

References 39 publications

Noise-tolerant NMF-based parallel algorithm for respiratory rate estimation

Noise-tolerant NMF-based parallel algorithm for respiratory rate estimation

Accurate Localization of First and Second Heart Sounds via Template Matching in Forcecardiography Signals

Classifying the heart sound signals using textural‐based features for an efficient decision support system

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