Computer methods and programs in biomedicine · 2025
Quantum LBP-driven heart sound analysis with quality assessment in real-world noisy environments.
СтатьяНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Sahoo Subhashree, Jain Puneet Kumar
Аннотация
BACKGROUND AND OBJECTIVE: Cardiovascular disease (CVD) is a major global health concern with increasing prevalence. While electrocardiography (ECG) and echocardiography offer high accuracy, their reliance on specialized equipment and trained personnel makes them costly and less accessible in rural areas. In contrast, the Phonocardiography (PCG) provides a more affordable alternative via capturing heart sound signals using a stethoscope. However, PCG analysis is often compromised by environmental noise. Most existing methods address this issue with denoising techniques, which can inadvertently suppress vital heart sound components. To address this challenge, the objective of this work is to develop a computationally efficient and noise-resilient method for PCG classification.
METHODS: The proposed approach introduces a quality assessment metric (PCGQA) to select the least noisy subsequences for further processing. For noise-robust feature extraction, an Enhanced Quantum Local Binary Pattern (EQLBP) method is employed, which adaptively selects reference pixels and extracts uniform patterns from the signal spectrogram to mitigate noise effects. In addition, Discrete Wavelet Transform (DWT) features are extracted to capture multi-resolution time-frequency characteristics, complementing the local texture features obtained from EQLBP. The combined feature set is then used to train conventional machine learning models.
RESULTS: The proposed method was evaluated using 10-fold cross-validation on two publicly available datasets: CinC-2016 and HSM-2018. On CinC-2016, it achieved an accuracy of 97.22%, precision of 98.29%, recall of 98.63%, and an F1-score of 98.46%. On HSM-2018, the method obtained an accuracy of 98.70%, precision of 99.05%, recall of 99.00%, and an F1-score of 99.00%. These results highlight the superior performance of the proposed approach compared to existing methods.
CONCLUSIONS: With its computational efficiency and robust performance, the proposed method is well-suited for out-of-clinic applications, particularly in rural and remote areas where access to advanced diagnostic tools is limited.
METHODS: The proposed approach introduces a quality assessment metric (PCGQA) to select the least noisy subsequences for further processing. For noise-robust feature extraction, an Enhanced Quantum Local Binary Pattern (EQLBP) method is employed, which adaptively selects reference pixels and extracts uniform patterns from the signal spectrogram to mitigate noise effects. In addition, Discrete Wavelet Transform (DWT) features are extracted to capture multi-resolution time-frequency characteristics, complementing the local texture features obtained from EQLBP. The combined feature set is then used to train conventional machine learning models.
RESULTS: The proposed method was evaluated using 10-fold cross-validation on two publicly available datasets: CinC-2016 and HSM-2018. On CinC-2016, it achieved an accuracy of 97.22%, precision of 98.29%, recall of 98.63%, and an F1-score of 98.46%. On HSM-2018, the method obtained an accuracy of 98.70%, precision of 99.05%, recall of 99.00%, and an F1-score of 99.00%. These results highlight the superior performance of the proposed approach compared to existing methods.
CONCLUSIONS: With its computational efficiency and robust performance, the proposed method is well-suited for out-of-clinic applications, particularly in rural and remote areas where access to advanced diagnostic tools is limited.