Phonocardiogram Signal Processing Module for Auto-Diagnosis and Telemedicine Applications

Moukadem, Ali; Dieterlen, Alain; Brandt, Christian

doi:10.5772/48447

Cited by 4 publications

(4 citation statements)

References 28 publications

(27 reference statements)

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“…In the PhysioNet database, most abnormal instances are related to heart valve defects and coronary artery disease patients (PhysioNet, 2016). The presence of murmurs increases the heart sound complexity (Moukadem, Dieterlen, & Brandt, 2013). This difference leads to a 30 dB difference (Figure 2) between the "normal" and the " abnormal " signal spectra.…”

Section: Deep Learning For Heart Sound Anomaly Detectionmentioning

confidence: 99%

“…This capability of deep networks makes them the right tool with which to extract and learn particular patterns from diverse and relatively large training sets, such as heart sound databases (PhysioNet, 2016). However most existing AI-assisted heart sound analysis systems use medically incomprehensible mathematical and statistical features, such as wavelets (Clifford, 2016), Stockwell transformation (Moukadem, Dieterlen, & Brandt, 2013), Mel-frequency cepstral coefficients (MFCCs) (Chen, 2017), and the likelihood function (Yamashita, Himeshima, & Matsunaga, 2014).…”

Section: Introductionmentioning

confidence: 99%

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Two-stage Artificial Intelligence Clinical Decision Support System for Cardiovascular Assessment using Convolutional Neural Networks and Decision Trees

Pasha

Lundgren

Carratù

et al. 2020

Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies

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This paper describes an artificial-intelligence-assisted screening system implemented to support medical cardiovascular examinations performed by doctors. The proposed system is a two-stage supervised classifier comprising a convolutional neural network for heart murmur detection and a decision tree for classifying vital signs. The classifiers are trained to prioritize higher-risk individuals for more time-efficient assessment. A feature selection approach is applied to maximize classification accuracy by using only the most significant vital signs correlated with heart issues. The results suggest that the trained convolutional neural network can learn and detect heart sound anomalies from the time-domain and frequency-domain signals without using any user-guided mathematical or statistical features. It is also concluded that the proposed two-stage approach improves diagnostic reliability and efficiency.

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Section: Deep Learning For Heart Sound Anomaly Detectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Two-stage Artificial Intelligence Clinical Decision Support System for Cardiovascular Assessment using Convolutional Neural Networks and Decision Trees

Pasha

Lundgren

Carratù

et al. 2020

Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies

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show abstract

“…Methods for segmentation often involve choosing a signal representation, such as the Homomorphic transform [16], [17], continuous wavelet transform [18], [19], Hilbert Transform [20] or Stockwell transform [21], [22]. A threshold is set on the output signal to detect S 1 and S 2 events.…”

Section: Introductionmentioning

confidence: 99%

Monitoring Cardiac Stress Using Features Extracted From S1 Heart Sounds

Herzig

Bickel

Eitan

et al. 2015

IEEE Trans. Biomed. Eng.

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It is known that acoustic heart sounds carry significant information about the mechanical activity of the heart. In this paper, we present a novel type of cardiac monitoring based on heart sound analysis. Specifically, we study two morphological features and their associations with physiological changes from the baseline state. The framework is demonstrated on recordings during laparoscopic surgeries of 15 patients. Insufflation, which is performed during laparoscopic surgery, provides a controlled, externally induced cardiac stress, enabling an analysis of each patient with respect to their own baseline. We demonstrate that the proposed features change during cardiac stress, and the change is more significant for patients with cardiac problems. Furthermore, we show that other well-known ECG morphology features are less sensitive in this specific cardiac stress experiment.

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“…The healthcare sector is entering the age of global deep networks makes them the right tool with which to extract and learn particular patterns from diverse and relatively large training sets, such as heart sound databases (PhysioNet, 2016). However most existing AI-assisted heart sound analysis systems use medically incomprehensible mathematical and statistical features, such as wavelets (Clifford, 2016), Stockwell transformation (Moukadem, Dieterlen, & Brandt, 2013), Mel-frequency cepstral coefficients (MFCCs) (Chen, 2017), and the likelihood function (Yamashita, Himeshima, & Matsunaga, 2014).…”

Section: Introductionmentioning

confidence: 99%

Two-stage Artificial Intelligence Clinical Decision Support System for Cardiovascular Assessment using Convolutional Neural Networks and Decision Trees

Pasha,

Lundgren,

Carratù

et al. 2020

Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies

View full text Add to dashboard Cite

show abstract

Phonocardiogram Signal Processing Module for Auto-Diagnosis and Telemedicine Applications

Cited by 4 publications

References 28 publications

Two-stage Artificial Intelligence Clinical Decision Support System for Cardiovascular Assessment using Convolutional Neural Networks and Decision Trees

Two-stage Artificial Intelligence Clinical Decision Support System for Cardiovascular Assessment using Convolutional Neural Networks and Decision Trees

Monitoring Cardiac Stress Using Features Extracted From S1 Heart Sounds

Two-stage Artificial Intelligence Clinical Decision Support System for Cardiovascular Assessment using Convolutional Neural Networks and Decision Trees

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