A Robust Interpretable Deep Learning Classifier for Heart Anomaly Detection Without Segmentation

Traditionally, abnormal heart sound classification is framed as a three-stage process. The first stage involves segmenting the phonocardiogram to detect fundamental heart sounds; after which features are extracted and classification is performed. Some researchers in the field argue the segmentation step is an unwanted computational burden, whereas others embrace it as a prior step to feature extraction. When comparing accuracies achieved by studies that have segmented heart sounds before analysis with those who have overlooked that step, the question of whether to segment heart sounds before feature extraction is still open. In this study, we explicitly examine the importance of heart sound segmentation as a prior step for heart sound classification, and then seek to apply the obtained insights to propose a robust classifier for abnormal heart sound detection. Furthermore, recognizing the pressing need for explainable Artificial Intelligence (AI) models in the medical domain, we also unveil hidden representations learned by the classifier using model interpretation techniques. Experimental results demonstrate that the segmentation plays an essential role in abnormal heart sound classification. Our new classifier is also shown to be robust, stable and most importantly, explainable, with an accuracy of almost 100% on the widely used PhysioNet dataset

Domain Generalization in Biosignal Classification

Objective: When training machine learning models, we often assume that the training data and evaluation data are sampled from the same distribution. However, this assumption is violated when the model is evaluated on another unseen but similar database, even if that database contains the same classes. This problem is caused by domain-shift and can be solved using two approaches: domain adaptation and domain generalization. Simply, domain adaptation methods can access data from unseen domains during training; whereas in domain generalization, the unseen data is not available during training. Hence, domain generalization concerns models that perform well on inaccessible, domain-shifted data. Method: Our proposed domain generalization method represents an unseen domain using a set of known basis domains, afterwhich we classify the unseen domain using classifier fusion. To demonstrate our system, we employ a collection of heart sound databases that contain normal and abnormal sounds (classes). Results: Our proposed classifier fusion method achieves accuracy gains of up to 16% for four completely unseen domains. Conclusion: Recognizing the complexity induced by the inherent temporal nature of biosignal data, the two-stage method proposed in this study is able to effectively simplify the whole process of domain generalization while demonstrating good results on unseen domains and the adopted basis domains. Significance: To our best knowledge, this is the first study that investigates domain generalization for biosignal data. Our proposed learning strategy can be used to effectively learn domain-relevant features while being aware of the class differences in the data

Feature extraction from MRI ADC images for brain tumor classification using machine learning techniques

Diffusion-weighted (DW) imaging is a well-recognized magnetic resonance imaging (MRI) technique that is being routinely used in brain examinations in modern clinical radiology practices. This study focuses on extracting demographic and texture features from MRI Apparent Diffusion Coefficient (ADC) images of human brain tumors, identifying the distribution patterns of each feature and applying Machine Learning (ML) techniques to differentiate malignant from benign brain tumors

Generalized Generative Deep Learning Models for Biosignal Synthesis and Modality Transfer

Generative Adversarial Networks (GANs) are a revolutionary innovation in machine learning that enables the generation of artificial data. Artificial data synthesis is valuable especially in the medical field where it is difficult to collect and annotate real data due to privacy issues, limited access to experts, and cost. While adversarial training has led to significant breakthroughs in the computer vision field, biomedical research has not yet fully exploited the capabilities of generative models for data generation, and for more complex tasks such as biosignal modality transfer. We present a broad analysis on adversarial learning on biosignal data. Our study is the first in the machine learning community to focus on synthesizing 1D biosignal data using adversarial models. We consider three types of deep generative adversarial networks: a classical GAN, an adversarial AE, and a modality transfer GAN; individually designed for biosignal synthesis and modality transfer purposes. We evaluate these methods on multiple datasets for different biosignal modalites, including phonocardiogram (PCG), electrocardiogram (ECG), vectorcardiogram and 12-lead electrocardiogram. We follow subject-independent evaluation protocols, by evaluating the proposed models' performance on completely unseen data to demonstrate generalizability. We achieve superior results in generating biosignals, specifically in conditional generation, by synthesizing realistic samples while preserving domain-relevant characteristics. We also demonstrate insightful results in biosignal modality transfer that can generate expanded representations from fewer input-leads, ultimately making the clinical monitoring setting more convenient for the patient. Furthermore our longer duration ECGs generated, maintain clear ECG rhythmic regions, which has been proven using ad-hoc segmentation models.</p

DConv-LSTM-Net: A Novel Architecture for Single and 12-Lead ECG Anomaly Detection

Electrocardiograms (ECGs) can be considered a viable method for cardiovascular disease (CVD) diagnosis. Recently, machine learning algorithms such as deep neural networks trained on ECG signals have demonstrated the capability to identify CVDs. However, existing models for ECG anomaly detection learn from relatively long (60 s) ECG signals and tend to be heavily parameterized. Thus, they require large time and computational resources during training. To address this, we propose a novel deep learning architecture that exploits dilated convolution layers. Our architecture benefits from a classical ResNet-like formulation, and we introduce a recurrent component to better leverage temporal information in the data, while also benefiting from the dilated convolution operation. Our proposed architecture is capable of learning from single-and 12-lead ECG signals and thus offers a flexible solution for CVD diagnosis. In our experiments, we perform subject-independent ten-fold cross-validations (CVs) and compare our results with two existing benchmark models using the PhysioNet atrial fibrillation (AF) challenge dataset, the China Physiological challenge, the PTB-XL repository from PhysioNet, and the Georgia dataset. For all the four datasets, our model archives state-of-the-art performance, with an upto 8% F1 score gain achieved. Our neural conduction plots demonstrate the effectiveness of having convolution layers with varying dilation factors and the use of recurrent networks to capture rhythmic patterns. Our architecture is explainable and has the ability to learn from short ECG segments. Using neural conductance, we reveal interesting hidden patterns learned by our model, which reflect the medical phenomena/characteristics associated with CVD. Code is publically available here.</p

Deep Learning for Patient-Independent Epileptic Seizure Prediction Using Scalp EEG Signals

Epilepsy is one of the most prevalent neurological diseases among humans and can lead to severe brain injuries, strokes, and brain tumors. Early detection of seizures can help to mitigate injuries, and can be used to aid the treatment of patients with epilepsy. The purpose of a seizure prediction system is to successfully identify the pre-ictal brain stage, which occurs before a seizure event. Patient-independent seizure prediction models are designed to offer accurate performance across multiple subjects within a dataset, and have been identified as a real-world solution to the seizure prediction problem. However, little attention has been given for designing such models to adapt to the high inter-subject variability in EEG data. We propose two patient-independent deep learning architectures with different learning strategies that can learn a global function utilizing data from multiple subjects. Proposed models achieve state-of-the-art performance for seizure prediction on the CHB-MIT-EEG dataset, demonstrating 88.81% and 91.54% accuracy respectively. In conclusion, the Siamese model trained on the proposed learning strategy is able to learn patterns related to patient variations in data while predicting seizures. Our models show superior performance for patient-independent seizure prediction, and the same architecture can be used as a patient-specific classifier after model adaptation. We are the first study that employs model interpretation to understand classifier behavior for the task for seizure prediction, and we also show that the MFCC feature map utilized by our models contains predictive biomarkers related to interictal and pre-ictal brain states.</p

Multi-stage stacked temporal convolution neural networks (MS-S-TCNs) for biosignal segmentation and anomaly localization

In the computer vision domain, temporal convolution networks (TCN) have gained traction due to their lightweight, robust architectures for sequence-to-sequence prediction tasks. With that insight, in this study, we propose a novel deep learning architecture for biosignal segmentation and anomaly localization based on TCNs, named the multi-stage stacked TCN, which employs multiple TCN modules with varying dilation factors. More precisely, for each stage, our architecture uses TCN modules with multiple dilation factors, and we use convolution-based fusion to combine predictions returned from each stage. Furthermore, aiming smoothed predictions, we introduce a novel loss function based on the first-order derivative. To demonstrate the robustness of our architecture, we evaluate our model on five different tasks related to three 1D biosignal modalities (heart sounds, lung sounds and electrocardiogram). Our proposed framework achieves state-of-the-art performance for all tasks, significantly outperforming the respective state-of-the-art models having F1 score gains up to ≈9%. Furthermore, the framework demonstrates competitive performance gains compared to traditional multi-stage TCN models with similar configurations yielding F1 score gains up to ≈5%. Our model is also interpretable. Using neural conductance, we demonstrate the effectiveness of having TCNs with varying dilation factors. Our visualizations show that the model benefits from feature maps captured at multiple dilation factors, and the information is effectively propagated through the network such that the final stage produces the most accurate result.</p

SigRep: Towards Robust Wearable Emotion Recognition with Contrastive Representation Learning

Extracting emotions from physiological signals has become popular over the past decade. Recent advancements in wearable smart devices have enabled capturing physiological signals continuously and unobtrusively. However, signal readings from different smart wearables are lossy due to user activities, making it difficult to develop robust models for emotion recognition. Also, the limited availability of data labels is an inherent challenge for developing machine learning techniques for emotion classification. This paper presents a novel self-supervised approach inspired by contrastive learning to address the above challenges. In particular, our proposed approach develops a method to learn representations of individual physiological signals, which can be used for downstream classification tasks. Our evaluation with four publicly available datasets shows that the proposed method surpasses the emotion recognition performance of state-of-the-art techniques for emotion classification. In addition, we show that our method is more robust to losses in the input signal

Domain Generalization in Biosignal Classification

Objective: When training machine learning models, we often assume that the training data and evaluation data are sampled from the same distribution. However, this assumption is violated when the model is evaluated on another unseen but similar database, even if that database contains the same classes. This problem is caused by domain-shift and can be solved using two approaches: domain adaptation and domain generalization. Simply, domain adaptation methods can access data from unseen domains during training; whereas in domain generalization, the unseen data is not available during training. Hence, domain generalization concerns models that perform well on inaccessible, domain-shifted data. Method: Our proposed domain generalization method represents an unseen domain using a set of known basis domains, afterwhich we classify the unseen domain using classifier fusion. To demonstrate our system, we employ a collection of heart sound databases that contain normal and abnormal sounds (classes). Results: Our proposed classifier fusion method achieves accuracy gains of up to 16% for four completely unseen domains. Conclusion: Recognizing the complexity induced by the inherent temporal nature of biosignal data, the two-stage method proposed in this study is able to effectively simplify the whole process of domain generalization while demonstrating good results on unseen domains and the adopted basis domains. Significance: To our best knowledge, this is the first study that investigates domain generalization for biosignal data. Our proposed learning strategy can be used to effectively learn domain-relevant features while being aware of the class differences in the data.</p