March 8, 2025Scientific ReportsOpen Access

The proposed CP-SBI-DCNN model utilizing both ECG and PCG signals achieved a classification accuracy of 97% with a 0.03 error rate for multi-class heart disease detection.

Q: What is the clinical evidence from this study?

Study design: Other. Population: Cardiovascular diseases. Intervention: CP-SBI-DCNN model using fused ECG and PCG signals vs. Existing models (DCNN, CNN, RBN, DNN). Primary outcome: Classification accuracy.

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Why the study?

Existing methods rely on either ECG or PCG signals alone, leading to higher false positive rates and incomplete cardiac evaluations.

Population

ECG and PCG signals from CirCor DigiScope Phonocardiogram Dataset, PTB-XL, and Physionet database…

Design

Other

Key result

The proposed CP-SBI-DCNN model utilizing both ECG and PCG signals achieved a classification accuracy of 97% with a 0.03 error rate for multi-class heart disease detection.

Authors

SHShivalila Hangaragi NNN. Neelima KJKatarina Jegdić

Discussion

Member takes

Overview

Supports ECG-PCG fusion models in research; leaves open clinical adoption pending prospective validation.

Structured PICO

Population

ECG and PCG signals from public databases used to train and validate a deep learning model for multi-class heart disease classification.

Intervention

Multi-class heart disease classification model utilizing both ECG and PCG signals with a C squared Pool Sign BI-power-activated Deep Convolutional Neural Network (CP-SBI-DCNN).

Outcome

Classification accuracy and error rate for identifying heart diseases.

Main Result

Absolute Event Rate: 97% vs 89%

An integrated deep learning model fusing ECG and PCG signals achieved 97% accuracy in classifying multiple heart diseases, including valvular disorders, atrial fibrillation, and ischemic heart disease.

Limitations

Leftover noise from outside factors such as patient movement, ambient noise, or inaccurate sensors
Variation in ECG and PCG signal properties brought on by age, gender, body composition, and comorbidities

Cite This Study

Hangaragi et al. (2025) studied Cardiovascular diseases. CP-SBI-DCNN model using fused ECG and PCG signals vs. Existing models (DCNN, CNN, RBN, DNN) was evaluated on Classification accuracy. The proposed CP-SBI-DCNN model utilizing both ECG and PCG signals achieved a classification accuracy of 97% with a 0.03 error rate for multi-class heart disease detection.

synapsesocial.com/papers/6a6e94f4e36a167817e0927c https://doi.org/10.1038/s41598-025-92395-w

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March 8, 2025Scientific ReportsOpen Access

The proposed CP-SBI-DCNN model utilizing both ECG and PCG signals achieved a classification accuracy of 97% with a 0.03 error rate for multi-class heart disease detection.

View Full Paper

Ask AI

Bookmark

Why the study?

Existing methods rely on either ECG or PCG signals alone, leading to higher false positive rates and incomplete cardiac evaluations.

Population

ECG and PCG signals from CirCor DigiScope Phonocardiogram Dataset, PTB-XL, and Physionet database…

Design

Other

Key result

The proposed CP-SBI-DCNN model utilizing both ECG and PCG signals achieved a classification accuracy of 97% with a 0.03 error rate for multi-class heart disease detection.

Authors

SHShivalila Hangaragi NNN. Neelima KJKatarina Jegdić

Discussion

Member takes

Overview

Supports ECG-PCG fusion models in research; leaves open clinical adoption pending prospective validation.

Structured PICO

Population

ECG and PCG signals from public databases used to train and validate a deep learning model for multi-class heart disease classification.

Intervention

Multi-class heart disease classification model utilizing both ECG and PCG signals with a C squared Pool Sign BI-power-activated Deep Convolutional Neural Network (CP-SBI-DCNN).

Outcome

Classification accuracy and error rate for identifying heart diseases.

Main Result

Absolute Event Rate: 97% vs 89%

Limitations

Leftover noise from outside factors such as patient movement, ambient noise, or inaccurate sensors
Variation in ECG and PCG signal properties brought on by age, gender, body composition, and comorbidities

Cite This Study

synapsesocial.com/papers/6a6e94f4e36a167817e0927c https://doi.org/10.1038/s41598-025-92395-w

Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

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