A Noise-Robust Heart Sound Segmentation Algorithm Based on Shannon Energy

Hybrid deep learning for computational precision in cardiac MRI segmentation: Integrating Autoencoders, CNNs, and RNNs for enhanced structural analysis

Recent advancements in cardiac imaging have been significantly enhanced by integrating deep learning models, offering transformative potential in early diagnosis and patient care. The research paper explores the application of hybrid deep learning methodologies, focusing on the roles of Autoencoders, Convolutional Neural Networks (CNNs), and Recurrent Neural Networks (RNNs) in enhancing cardiac image analysis. The study implements a comprehensive approach, combining traditional algorithms such as Sobel, Watershed, and Otsu's Thresholding with advanced deep learning models to achieve precise and accurate imaging outcomes. The Autoencoder model, developed for image enhancement and feature extraction, achieved a notable accuracy of 99.66% on the test data. Optimized for image recognition tasks, the CNN model demonstrated a high precision rate of 98.9%. The RNN model, utilized for sequential data analysis, showed a prediction accuracy of 98%, further underscoring the robustness of the hybrid framework. The research drew upon a diverse range of academic databases and pertinent publications within cardiac imaging and deep learning, focusing on peer-reviewed articles and studies published in the past five years. Models were implemented using the TensorFlow and Keras frameworks. The proposed methodology was evaluated in the clinical validation phase using advanced imaging protocols, including the QuickScan technique and balanced steady-state free precession (bSSFP) imaging. The validation metrics were promising: the Signal-to-Noise Ratio (SNR) was improved by 15%, the Contrast-to-Noise Ratio (CNR) saw an enhancement of 12%, and the ejection fraction (EF) analysis provided a 95% correlation with manually segmented data. These metrics confirm the efficacy of the models, showing significant improvements in image quality and diagnostic accuracy. The integration of adversarial defense strategies, such as adversarial training and model ensembling, have been analyzed to enhance model robustness against malicious inputs. The reliability and comparison of the model's ability have been investigated to maintain clinical integrity, even in adversarial attacks that could otherwise compromise segmentation outcomes. These findings indicate that integrating Autoencoders, CNNs, and RNNs within a hybrid deep-learning framework is promising for enhancing cardiac MRI segmentation and early diagnosis. The study contributes to the field by demonstrating the applicability of these advanced techniques in clinical settings, paving the way for improved patient outcomes through more accurate and timely diagnoses.

Unsupervised segmentation of heart sounds from abrupt changes detection

BACKGROUND AND OBJECTIVE

Heart auscultation enables early diagnosis of cardiovascular diseases. Automated segmentation of cardiograms into fundamental heart states can guide physicians to analyze the patient's condition more effectively. In this work, we propose an unsupervised method of segmentation into heart sounds and silences based on the detection of abrupt changes in the signal.

METHODS

Our procedure involves two steps. First, the abrupt changes, which correspond to the beginning and end of the heart sounds, are localized. Heart sounds and silences are then identified by calculating the signal power in each interval defined by the change points. The parameters of our algorithm are adjusted on the basis of estimated heart rate alone.

RESULTS

We evaluate our method on three independent open-access database (PhysioNet 2016, CirCor DigiScope and PASCAL) for healthy and pathological populations, with or without murmurs. It achieves mean F1 score detection performance of 91.2%, 94.3% and 96.3% respectively, outperforming most of the competing unsupervised approaches.

CONCLUSION

By providing top ranking detection performance for three different types of heart sounds database, the proposed algorithm is reliable and robust, yet easy to implement.

SIGNIFICANCE

This paper presents a simple and effective alternative segmentation method that can help improve the physiological interpretation of heart sounds recordings.

Rapid detection and interpretation of heart murmurs using phonocardiograms, transfer learning and explainable artificial intelligence

Cardiovascular disease, which remains one of the main causes of death, can be prevented by early diagnosis of heart sounds. Certain noisy signals, known as murmurs, may be present in heart sounds. On auscultation, the degree of murmur is closely related to the patient's clinical condition. Computer-aided decision-making systems can help doctors to detect murmurs and make faster decisions. The Mel spectrograms were generated from raw phonocardiograms and then presented to the OpenL3 network for transfer learning. In this way, the signals were classified to predict the presence or absence of murmurs and their level of severity. Pitch level (healthy, low, medium, high) and Levine scale (healthy, soft, loud) were used. The results obtained without prior segmentation are very impressive. The model used was then interpreted using an Explainable Artificial Intelligence (XAI) method, Occlusion Sensitivity. This approach shows that XAI methods are necessary to know the features used internally by the artificial neural network then to explain the automatic decision taken by the model. The averaged image of the occlusion sensitivity maps can give us either an overview or a precise detail per pixel of the features used. In the field of healthcare, particularly cardiology, for rapid diagnostic and preventive purposes, this work could provide more detail on the important features of the phonocardiogram.