Automatic heart sound analysis with short-time Fourier transform and support vector machines

Kao, Wen-Chung; Wei, Chih-Chao; Liu, Jen-Jui; Hsiao, Pei‐Yung

doi:10.1109/mwscas.2009.5236120

Cited by 9 publications

(4 citation statements)

References 5 publications

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“…In one study, a couple of heart rate turbulence denoising methodologies were proposed and attempted with uncommon meticulousness to reinforce SVM estimation [23]. In an experiment conducted by a public heart sound database and released by the Texas Heart Institute, the kernels of heartbeat cycle segmentation and recognition were based on autocorrelation, short-time Fourier transform, and the SVM [24]. An SVM has been used to classify heartbeat time series, whereas statistical methods and signal analysis techniques were used to extract features from the signals [21].…”

Section: Literature Review and The State Of The Artmentioning

confidence: 99%

A Machine-Learning-Based Approach to Predict the Health Impacts of Commuting in Large Cities: Case Study of London

Tamang¹,

Sharif²,

Al-Bayatti

et al. 2020

Symmetry

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The daily commute represents a source of chronic stress that is positively correlated with physiological consequences, including increased blood pressure, heart rate, fatigue, and other negative mental and physical health effects. The purpose of this research is to investigate and predict the physiological effects of commuting in Greater London on the human body based on machine-learning approaches. For each participant, the data were collected for five consecutive working days, before and after the commute, using non-invasive wearable biosensor technology. Multimodal behaviour, analysis and synthesis are the subjects of major efforts in computing field to realise the successful human–human and human–agent interactions, especially for developing future intuitive technologies. Current analysis approaches still focus on individuals, while we are considering methodologies addressing groups as a whole. This research paper employs a pool of machine-learning approaches to predict and analyse the effect of commuting objectively. Comprehensive experimentation has been carried out to choose the best algorithmic structure that suit the problem in question. The results from this study suggest that whether the commuting period was short or long, all objective bio-signals (heat rate and blood pressure) were higher post-commute than pre-commute. In addition, the results match both the subjective evaluation obtained from the Positive and Negative Affect Schedule and the proposed objective evaluation of this study in relation to the correlation between the effect of commuting on bio-signals. Our findings provide further support for shorter commutes and using the healthier or active modes of transportation.

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Section: Literature Review and The State Of The Artmentioning

confidence: 99%

A Machine-Learning-Based Approach to Predict the Health Impacts of Commuting in Large Cities: Case Study of London

Tamang¹,

Sharif²,

Al-Bayatti

et al. 2020

Symmetry

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show abstract

“…This technique was chosen due to the fact that support vector machines have been shown to be effective in the classification of normal vs. abnormal phonocardiograms [11][12][13]. Seventy-four features were selected from both the time and frequency domains, with guidance provided by the prior studies as to which attributes would be most effective [11,14].…”

Section: Classificationmentioning

confidence: 99%

Automated Classification of Normal and Abnormal Heart Sounds using Support Vector Machines

Bouril¹,

Aleinikava

Guillem

et al. 2016

2016 Computing in Cardiology Conference (CinC)

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“…As for the analysis of HS, many approaches have been proposed in feature exaction in the literatures such as wavelet decomposition and reconstruction method [1]- [3], short time Fourier transform (STFT) method [4], [5], and S-transform method [6], [7]. Most solely analyze the time frequency domain for feature extractions.…”

Section: Introductionmentioning

confidence: 99%

Multidimensional Feature Extraction Based on Timbre Model for Heart Sound Analysis

Wang¹,

Li²,

Fu³

et al. 2014

IJBBB

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Abstract-Cardiovascular diseases (CVDs) are currently the leading cause of deaths worldwide. The traditional auscultation is cost-effective and time-saving for the public to diagnose CVDs early. While many approaches for analysis of the heart sound (HS) signal from auscultation have been utilized successfully, few studies are focused on acoustic perspective to interpret the HS signal. This paper creatively proposes a multidimensional feature extraction technique based on timbre model to interpret HS, which stems from clinical diagnostic basis and medical knowledge. The extracted features have three dimensions, including spectral centroid (SC), log attack time (LT) and temporal centroid (TC). The simulation experiments indicate that the proposed method is promising in HS feature extraction and the later CVD diagnosis.Index Terms-Acoustics, feature extraction, Hilbert transform, spectral centroid, temporal centroid. I. INTRODUCTIONCardiovascular diseases (CVDs) have become a great threat to human' lives aggressively. A good way to prevent the death caused by CVDs is early discovery and interposition. Over the years, in spite of the advent of echocardiogram (ECHO), electrocardiogram (ECG), photoplethysmogram (PPG), sphygmogram (SPG), etc., the heart auscultation signal is still one of the most primary physiological signals. Physicians primarily analyze it thus to find signs of pathologic conditions, as it can provide clues to the diagnosis of many cardiac abnormalities including valvular heart disease, congestive heart failure and congenital heart lesions before requesting for ECHO, ECG, PPG, SPG, etc. Notably, in remote areas or less developed regions, auscultation may be the only means available. This leads the research on heart sound (HS) signal analysis and interpretation in order to provide a cost-effective and time-saving prognostic approach for the victims of CVDs.As for the analysis of HS, many approaches have been proposed in feature exaction in the literatures such as wavelet decomposition and reconstruction method . MFCC is based on the theory that human audition spaces are linearly at low frequency band and logarithmically at high frequency. As existing two inverse-transforms in MFCC, it encounters computation complexity.There exists another approach named timbre analysis from acoustic perspective, which hasn't been utilized and can avoid computation complexity. In acoustics, timbre is a significant attribute of three acoustic attributes, which embodies the texture of acoustic source. CVDs, as the pathological changes in the heart and the blood vessels, provide different timbre information. As a result, the timbre is well suitable for HS feature extractions. However, current timbre model analysis all aim at different music instrument recognition and little literature ever reported its exploration on HS feature extraction. In this paper, the timbre model based multidimensional feature extraction technique for HS analysis is creatively proposed. It is observed from simulations that the proposed method is ...

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Automatic heart sound analysis with short-time Fourier transform and support vector machines

Cited by 9 publications

References 5 publications

A Machine-Learning-Based Approach to Predict the Health Impacts of Commuting in Large Cities: Case Study of London

A Machine-Learning-Based Approach to Predict the Health Impacts of Commuting in Large Cities: Case Study of London

Automated Classification of Normal and Abnormal Heart Sounds using Support Vector Machines

Multidimensional Feature Extraction Based on Timbre Model for Heart Sound Analysis

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