arXiv subjects

Gari D Clifford

Publications and source records attributed to Gari D Clifford.

3 recordsLinked to original sources

arXiv2025

A Noise-Robust Model-Based Approach to T-Wave Amplitude Measurement and Alternans Detection

T-wave alternans (TWA) is a potential marker for sudden cardiac death, but its reliable analysis is often constrained to noise-free environments, limiting its utility in real-world settings. We explore model-based T-wave estimation to mitigate the impact of noise on TWA level. Detection was performed using a previous surrogate-based method as a benchmark and a new method based on a Markov model state transition matrix (STM). These were combined with a Modified Moving Average (MMA) method and polynomial T-wave modeling to enhance noise robustness. Methods were tested across a wide range of signal-to-noise ratios (SNRs), from -5 to 30 dB, and different noise types: baseline wander (BW), muscle artifacts (MA), electrode movement (EM), and respiratory modulation. Synthetic ECGs with known TWA levels were used: 0 uV for TWA-free and 30-72 uV for TWA-present signals. T-wave modeling improved estimation accuracy under noisy conditions. With EM noise at SNRs of -5 and 5 dB, mean absolute error (MAE) dropped from 62 to 49 uV and 27 to 25 uV, respectively (Mann-Whitney-U test, p < 0.05) with modeling applied. Similar improvements were seen with MA noise: MAE dropped from 100 to 70 uV and 26 to 23 uV. In detection, the STM method achieved an F1-score of 0.92, outperforming the surrogate-based method (F1 = 0.81), though both struggled under EM noise at -5 dB. Importantly, beyond SNR, detection performance depended on the number of beats analyzed. These findings show that combining model-based estimation with STM detection significantly improves TWA analysis under noise, supporting its application in ambulatory and wearable ECG monitoring.

physics.med-ph↗

arXiv2024

Feasibility of assessing cognitive impairment via distributed camera network and privacy-preserving edge computing

INTRODUCTION: Mild cognitive impairment (MCI) is characterized by a decline in cognitive functions beyond typical age and education-related expectations. Since, MCI has been linked to reduced social interactions and increased aimless movements, we aimed to automate the capture of these behaviors to enhance longitudinal monitoring. METHODS: Using a privacy-preserving distributed camera network, we collected movement and social interaction data from groups of individuals with MCI undergoing therapy within a 1700$m^2$ space. We developed movement and social interaction features, which were then used to train a series of machine learning algorithms to distinguish between higher and lower cognitive functioning MCI groups. RESULTS: A Wilcoxon rank-sum test revealed statistically significant differences between high and low-functioning cohorts in features such as linear path length, walking speed, change in direction while walking, entropy of velocity and direction change, and number of group formations in the indoor space. Despite lacking individual identifiers to associate with specific levels of MCI, a machine learning approach using the most significant features provided a 71% accuracy. DISCUSSION: We provide evidence to show that a privacy-preserving low-cost camera network using edge computing framework has the potential to distinguish between different levels of cognitive impairment from the movements and social interactions captured during group activities.

cs.AI↗

arXiv2021

The CirCor DigiScope Dataset: From Murmur Detection to Murmur Classification

Cardiac auscultation is one of the most cost-effective techniques used to detect and identify many heart conditions. Computer-assisted decision systems based on auscultation can support physicians in their decisions. Unfortunately, the application of such systems in clinical trials is still minimal since most of them only aim to detect the presence of extra or abnormal waves in the phonocardiogram signal, i.e., only a binary ground truth variable (normal vs abnormal) is provided. This is mainly due to the lack of large publicly available datasets, where a more detailed description of such abnormal waves (e.g., cardiac murmurs) exists. To pave the way to more effective research on healthcare recommendation systems based on auscultation, our team has prepared the currently largest pediatric heart sound dataset. A total of 5282 recordings have been collected from the four main auscultation locations of 1568 patients, in the process, 215780 heart sounds have been manually annotated. Furthermore, and for the first time, each cardiac murmur has been manually annotated by an expert annotator according to its timing, shape, pitch, grading, and quality. In addition, the auscultation locations where the murmur is present were identified as well as the auscultation location where the murmur is detected more intensively. Such detailed description for a relatively large number of heart sounds may pave the way for new machine learning algorithms with a real-world application for the detection and analysis of murmur waves for diagnostic purposes.

q-bio.QM↗