Open source ECG analysis

By P. Hamilton. Computers in cardiology, 2002

455 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Cardiology and Cardiovascular Medicine, Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/616e2a9ac12789255215859053e20ee1934a85c6

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Biomedical Engineering

In Cardiology and Cardiovascular Medicine

In Cognitive Neuroscience

Citations in the corpus

Deep PPG: Large-Scale Heart Rate Estimation with Convolutional Neural Networks (2019, methodology): “First, we used a well-known R-peak detector [40].”
A Wearable Smartphone-Based Platform for Real-Time Cardiovascular Disease Detection Via Electrocardiogram Processing (2010, methodology): “Furthermore, the ECG signal from the monitoring session can be recorded to a secure digital (SD) card that plugs into the sensor, allowing for optional offline analysis by a physician similar to that of a Holter monitor.”
ECG-based heartbeat classification for arrhythmia detection: A survey (2016, unknown): “[48] P.S. Hamilton, W.J. Tompkins, Quantitative investigation of QRS detection rules using the MIT/BIH arrhythmia database, IEEE Trans.”
A wavelet-based ECG delineator: evaluation on standard databases (2004, methodology): “On the MITDB (widely used to evaluate QRS detectors), only three detectors based on WT ( [19], [21] and this paper) and a very recent one based on more classical approaches [ 46 ], report and over 99.8%.”
ECG signal analysis through hidden Markov models (2006, unknown): “In general, the classification approaches are heuristic [12]–[14], namely decision trees and fuzzy logic [15], and statistics, namely discriminant analysis [15], hidden Markov models (HMMs) [16], neural networks [17]–[21], and statistical ruled based systems [22].”
Exploring Physiological Correlates of Visual Complexity Adaptation: Insights from EDA, ECG, and EEG Data for Adaptation Evaluation in VR Adaptive Systems (2023, methodology): “We first filtered the ECG signal by the Finite Impulse Response (FIR) band-pass filter (3–45 Hz, 3rd order), and then segmented by Hamilton’s method [31] to identify the QRS complexes and extract mean Heart Rate (HR) and Heart Rate Variability (HRV), defined as the root mean square of the successive”