Biomedical Commons^™

Dot Product Engine Based Neuromorphic Hardware For Continuous-Time Biomedical Signal Classification, Sanjeev Srinivasan

Master's Theses

Accurate diagnosis of pathological conditions from biomedical signals, such as electrocardiograms (ECGs) is often performed offline, making it time-consuming, costly, and inefficient, especially when abnormal patterns are rare and long-term monitoring generates large amounts of data. To address this, this work proposes a compact, scalable, and programmable neuromorphic system designed for real-time preliminary arrhythmia detection and classification using ECG signals, that can be extended to other biomedical signals. The proposed design processes ECG signals using a delta modulation-based spike encoder, followed by classification with a dot-product engine (DPE) based spiking neural network (SNN) processor and winner-take-all (WTA) circuit. The architecture …

Heartfev1: A Mobile Electrocardiogram Based System For Inferring Forced Expiratory Volume In One Second From Patients With Chronic Obstructive Pulmonary Disease, Maria Nyamukuru

Dartmouth College Ph.D Dissertations

Chronic Obstructive Pulmonary Disease (COPD), characterized by chronic airway inflammation and airflow obstruction, is the third leading cause of death globally. Patients with COPD experience exacerbated symptoms like breathlessness and cough, significantly impacting their quality of life and leading to costly hospitalizations. Early detection of COPD exacerbations is crucial for mitigating these negative effects.

The most critical element for early detection of COPD exacerbations is daily monitoring of lung function, particularly forced expiratory volume in one second (FEV1), a key metric of lung function. By tracking declines in FEV1, COPD exacerbations can be predicted up to two weeks in advance, …

Neural Network Pruning For Ecg Arrhythmia Classification, Isaac E. Labarge

Master's Theses

Convolutional Neural Networks (CNNs) are a widely accepted means of solving complex classification and detection problems in imaging and speech. However, problem complexity often leads to considerable increases in computation and parameter storage costs. Many successful attempts have been made in effectively reducing these overheads by pruning and compressing large CNNs with only a slight decline in model accuracy. In this study, two pruning methods are implemented and compared on the CIFAR-10 database and an ECG arrhythmia classification task. Each pruning method employs a pruning phase interleaved with a finetuning phase. It is shown that when performing the scale-factor pruning …

Portable Electrocardiogram Device And Signal Processing Design, Ryan F. Blaalid

Electrical Engineering

Full 12-lead electrocardiogram (ECG) measurements require inconvenient and time consuming adhesive electrode placement. This project proposes a design for a Bluetooth based ECG for remote patient measurement. The device is designed to measure up to 6-leads and utilizes 5 dry (non-adhesive) electrodes to accomplish this. The device delivers the ECG data to the user’s mobile smart phone and can then be sent to the patient’s doctor for analysis. Since the contact of the dry electrodes to the skin is not perfect, low-frequency noise called baseline wandering is introduced. A signal processing technique borrowed from image processing called morphological filtering is …

Ecg Classification Using Adaptive Neuro-Fuzzy Inference System, Jason N. Rivera, Kelsey C. Rodriguez

Electrical Engineering

ECG classification using Adaptive Neuro-Fuzzy Inference System (ANFIS), sponsored by Professor Yu, involves the diagnosis of six cardiovascular conditions by analyzing one single neural network. Today’s ECG signal instrumentation does not have the ability to characterize cardiovascular diseases without a doctor’s complete evaluation and diagnosis. Our project gives a promising solution to the inability in the current market’s ECG signal instrumentation to correctly evaluate and diagnose cardiovascular diseases. ECG signal reportings is a non-invasive process that will lead to many more applications of advanced signal processing and data analysis/diagnosis of cardiovascular diseases. The inputs to the ANFIS are annotations of …

A Software Application For Cardiac-Gated Computerized Tomography Scanning, Stephen Caldwell, Trevor Engelsman

Williams Honors College, Honors Research Projects

Computerized tomography (CT) scans are a common clinical imaging procedure used worldwide. Operating in the X-Ray spectrum, these machines rotate scanners around a stationary body in order to compile two-dimensional images into a unified three-dimensional image. With adjustment to scan frequency and intensity, internal features such as muscles, organs, and tendons can easily be viewed. However, the heart has long evaded CT use due to its near-constant motion. Recently, cardiac-gated scans have entered the market as a technique to image the heart at a specific moment in time when it is nearly still - the quiescent period. Occurring during rapid …

Ecg Classification With An Adaptive Neuro-Fuzzy Inference System, Brad Thomas Funsten

Master's Theses

Heart signals allow for a comprehensive analysis of the heart. Electrocardiography (ECG or EKG) uses electrodes to measure the electrical activity of the heart. Extracting ECG signals is a non-invasive process that opens the door to new possibilities for the application of advanced signal processing and data analysis techniques in the diagnosis of heart diseases. With the help of today’s large database of ECG signals, a computationally intelligent system can learn and take the place of a cardiologist. Detection of various abnormalities in the patient’s heart to identify various heart diseases can be made through an Adaptive Neuro-Fuzzy Inference System …

Concept Design Of A Labview-Mysql Data Warehouse For Digital Recording Of Ecg-Emg, Kristine R. Suratos, Rosula Sj Reyes

Electronics, Computer, and Communications Engineering Faculty Publications

To align with the recommendations set forth by the World Health Organization regarding eHealth, this study aims to develop a concept design of a suitable data warehouse for healthcare using electronic medical recording of ECG-EMG. This concept design once implemented aims to assist health care providers in improving patient’s healthcare data management. The system consists of three main layer: data source layer; software application layer; and data service database layer. The data source layer consists of patient profile, patient consultation, and patient test. Patient test was conducted using Olimex ECG-EMG shield and NI myDAQ module. The software application layer, developed …