ACM Transactions on Computing for Healthcare

Epilepsy is one of the most common neurological diseases globally (around 50M people globally). Fortunately, up to 70% of people with epilepsy could live seizure-free if properly diagnosed and treated, and a reliable technique to monitor the onset of seizures could improve the quality of life of patients who are constantly facing the fear of random seizure attacks. The current gold standard, video-EEG (v-EEG), involves attaching over 20 electrodes to the scalp, is costly, requires hospitalization, trained professionals, and is uncomfortable for patients. To address this gap, we developed EarSD , a lightweight and unobtrusive ear-worn system to detect seizure onsets by measuring physiological signals behind the ears. This system can be integrated into earphones, headphones, or hearing aids, providing a convenient solution for continuous monitoring. EarSD is an integrated custom-built sensing - computing - communication ear-worn platform to capture seizure signals, remove the noises caused by motion artifacts and environmental impacts, and stream the collected data wirelessly to the computer/mobile phone nearby. EarSD 's ML algorithm, running on a server, identifies seizure-associated signatures and detects onset events. We evaluated the proposed system in both in-lab and in-hospital experiments at the University of Texas Southwestern Medical Center with epileptic seizure patients, confirming its usability and practicality.

Abdul Aziz

Nhat Pham

Neel Vora

Cody Reynolds

Jaime Lehnen

Pooja Venkatesh

Zhuoran Yao

Jay Harvey

Tam Vu

Kan Ding

Phuc Nguyen

An Unobtrusive and Lightweight Ear-worn System for Continuous Epileptic Seizure Detection

一种不显眼且轻便的耳戴式系统用于持续癫痫发作检测

According to the Global Health Observatory (GHO), stroke ranks second worldwide in causing dementia, right after Alzheimer's disease. The mortality rate linked to dementia resulting from stroke is high because symptoms are often recognized late, and stroke can be misinterpreted as other brain disorders. Early detection and diagnosis of stroke is crucial. Therefore, increasing awareness of stroke symptoms and implementing preventive measures becomes imperative. Prompt intervention by healthcare professionals can improve outcomes and reduce long-term complications of stroke. The research introduces an innovative approach for early stroke prediction using a fuzzy scoring-based Decision Support System. This approach encompasses three main modules: Mind-map based Data Modeling, Fuzzy scoring computing, and ML-based Decision System. By incorporating fuzzy logic, the approach extracts valuable knowledge from imprecise and uncertain data. Combining the fuzzy stroke risk model with a machine learning-based decision support system aims to enhance stroke prediction accuracy and improve preventive measures and patient outcomes. The approach's effectiveness was validated using real clinical data and tested with various machine learning classifiers, including K-Nearest Neighbour (KNN), Logistic Regression (LR), Decision Tree (DT), Artificial Neural Network (ANN), and Support Vector Machine (SVM). The results showed a strong correlation between stroke cases and computed risk-scoring values. In comparison to predictions without fuzzy scoring and other related works, the stroke risk prediction using the proposed approach demonstrated higher accuracy, making it a promising method for early stroke detection and prevention.

Mayssa Ben Kahla

Dalel Kanzari

Sana Ben Amor

Sonia Ayachi Ghannouchi

Ricardo Martinho

Enhanced Fuzzy Score-Based Decision Support System for Early Stroke Prediction

基于模糊评分的早期中风预测决策支持系统增强研究

Dicrotic Notch (DN), one of the most significant and indicative features of the arterial blood pressure (ABP) waveform, becomes less pronounced and thus harder to identify as a matter of aging and pathological vascular stiffness. Generalizable and automatic DN identification for such edge cases is even more challenging in the presence of unexpected ABP waveform deformations that happen due to internal and external noise sources or pathological conditions that cause hemodynamic instability. We propose a physics-aware approach, named Physiowise (PW), that first employs a cardiovascular model to augment the original ABP waveform and reduce unexpected deformations, then apply a set of predefined rules on the augmented signal to find DN locations. We have tested the proposed method on in-vivo data gathered from 14 pigs under hemorrhage and sepsis study. Our result indicates 52% overall mean error improvement with 16% higher detection accuracy within the lowest permitted error range of 30 ms. An additional hybrid methodology is also proposed to allow combining augmentation with any application-specific user-defined rule set.

Mahya Saffarpour

Debraj Basu

Fatemeh Radaei

Kourosh Vali

Jason Y. Adams

Chen-Nee Chuah

Soheil Ghiasi

Physiowise: A Physics-aware Approach to Dicrotic Notch Identification

Physiowise：一种识别重搏切迹的物理感知方法

Breast cancer is a kind of cancer that forms in the cells of the breasts. After skin cancer, breast cancer represents the most common cancer diagnosed in women in the United States. As a matter of fact, in January 2022, there are more than 3.8 million women with a history of breast cancer in the United States, this is the reason why there is a need for novel methods for automatic breast cancer screening, with the aim of starting any therapy as quickly as possible to try to limit the proliferation of the disease. In this paper, we propose a method aimed at detecting breast cancer through a deep learning network developed by authors. Moreover, the proposed method is able to provide prediction explainability by means of class activation mapping, aimed to automatically highlight the suspicious area on the image. We take into account a way to understand whether the cancer prediction and localisation can be considered robust by analyzing the output of two different class activation mapping algorithms. We evaluate the effectiveness of the proposed method by using a dataset composed of 9016 images obtaining an accuracy equal to 93.5%, thus showing the effectiveness of the proposed network for breast cancer detection and localisation.

Marcello Di Giammarco

Camilla Vitulli

Simone Cirnelli

Benedetta Masone

Antonella santone

Mario Cesarelli

Fabio Martinelli

Francesco Mercaldo

Explainable Deep Learning for Breast Cancer Classification and Localisation

可解释深度学习在乳腺癌分类与定位中的应用

Deploying bio-electrical signals and image processing (visual) techniques are the two popular means to provide input to generate grasp control for robotic and prosthetic devices. Visual perception-based techniques rely on computationally expensive image processing algorithms and are affected by lighting conditions. In contrast, grasp control based on bio-electric signals such as surface electromyography (sEMG) is invariant to lighting conditions. It can reflect human intent to hand motion or grasp with lesser computational costs. In this article, we propose an efficient machine-learning pipeline to classify hand grasp using a minimal number of sEMG sensors. A cooperative game theory-based feature selection technique is applied to find the representative feature subset. The feature selection method uses a modified marginal contribution based on the class distribution coefficient to generate feature ranking. This feature ranking is further used to find the most representative feature subset from the extracted feature set. Our proposed pipeline has been evaluated on a benchmark dataset and has achieved a classification accuracy of 98.20%, using single-channel EMG when coupled with the XGBoost classifier. Thorough assessments were conducted to confirm the reliability of the results obtained. Our proposed pipeline holds the potential to facilitate the development of cost-effective sEMG prosthetics.

Shashank Kumar singh

Amrita Chaturvedi

A Cooperative game theory-based feature selection for efficient hand grasp classification using minimal number of sEMG signals

基于合作博弈论的特征选择方法用于高效手部抓取分类，使用最少量的sEMG信号

The landscape of healthcare is experiencing a digitalization shift, transferring many medical activities to the patients’ homes, a phenomenon commonly referred to as Hospital-at-Home. While Internet of Things (IoT) devices facilitate the building of such systems, there is a need for powerful middleware that encapsulates device-to-device communication, and enables the construction of user-friendly, secure, and robust Hospital-at-Home systems. A key challenge for such middleware is to build a trustworthy and lightweight key management system allowing different devices in the system to exchange messages securely. In this paper we present a simple, easily manageable and scalable such architecture which, in addition, supports long term data protection using post-quantum cryptographic primitives. Our proposed solution utilizes a Merkle tree to enable the IoT devices to establish trust between each other automatically, even in the absence of Internet connection. We have implemented the architecture and present performance figures as well as a security analysis of our approach.

ISSN	2637-8051, 2691-1957
h-index	16

出版信息	出版商: Association for Computing Machinery (ACM)，出版周期: ，期刊类型: journal，开源期刊: 否
基本数据	创刊年份: 2020，原创研究文献占比: ，自引率:， Gold OA占比:

ACM Transactions on Computing for Healthcare

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An Unobtrusive and Lightweight Ear-worn System for Continuous Epileptic Seizure Detection

Enhanced Fuzzy Score-Based Decision Support System for Early Stroke Prediction

Physiowise: A Physics-aware Approach to Dicrotic Notch Identification

Explainable Deep Learning for Breast Cancer Classification and Localisation

A Cooperative game theory-based feature selection for efficient hand grasp classification using minimal number of sEMG signals

Enhanced Fuzzy Score-Based Decision Support System for Early Stroke Prediction

An Unobtrusive and Lightweight Ear-worn System for Continuous Epileptic Seizure Detection

Explainable Deep Learning for Breast Cancer Classification and Localisation

A Cooperative game theory-based feature selection for efficient hand grasp classification using minimal number of sEMG signals

A Trust Establishment and Key Management Architecture for Hospital-at-Home