jackwboynton / ecg_alarm_classification_icu

Classification of True or False Arrhythmia ECG Alarms in the ICU

This repository contains supplementary materials from a research project for determining whether an ECG arrhythmia alarm in the ICM is a true alarm or a false alarm. This is done by classifying the ECG segment immediately following an alarm into either that of a true alarm or a false alarm, as accurately as possible and as early as possible. This research resulted in an advancement of the state of the art, mostly resulting from 2015 PhysioNet/CinC Challenge (https://www.physionet.org/content/challenge-2015/1.0.0/).

Contents in this repository:

Paper:

1. CinC 2021 abstract pdf
2. CinC 2021 poster pdf
3. CinC 2021 full paper pdf

Methods:

1. Deep learning model: ResNet + BiLSTM. Model architecture (figure below) and source code python (link)
2. Prequential evaluation: Growing window version. Source code python (link)
3. Data preparation: WFDB ECG Segment splitting and data preparation. Source code python (link)
4. Data sets: 2015 PhysioNet Challenge data sets (download)

Results:

The result datasets are those used to generate the figures and tables in the paper. (The corresponding figure/table numbers are specified for each dataset.)

1. Classification time for varying interval: one row for each ECG segment; one column for each batch-interval (4 msec, 0.5 sec, 1 sec, 2 sec) (download) (see Figure 3 in text).
2. Model's output probability over time: one row for each ECG segment; one row for each sample within the sample interval (4 msec) (download true alarm) (download false alarm)(see Figure 4 in text).
3. Classification times (with the 4 msec interval) for all ECG segments, comparing approaches. (download boxplots) (see Figure 5 in text)
4. Classification accuracy among the three model structures (ResNet+BiLSTM, ResNet only, BiLSTM only) (download table) (see Table 1 in text).