Experiment 11 — Cardiac Arrhythmia

Δ.72 on MIT-BIH ECG

Applying the coherence framework to cardiologist-annotated electrocardiograms. 45 ECG records at 360 Hz, 2 channels, 13,044 abnormal beats across 11 arrhythmia types. Can Δ detect cardiac rhythm departures from coherent baseline?

Δ = (P · A · R) / (D + N)
Applied to 2-channel ECG (modified limb lead MLII + V1/V5) sampled at 360 Hz.
Per-channel rolling-mean baseline (30s window). Coherence window: 1080 samples (3s), step 360 (1s). Per-channel Δ averaged. Δ threshold: 0.3.
39
ECG Records
36
With Arrhythmia
0.238
Mean F1 Score
0.267
Mean Precision
0.391
Mean Recall
317s
Mean Lead Time
0.967
Mean Δ Normal
0.889
Mean Δ Abnormal

Across 39 MIT-BIH records using per-channel rolling-baseline coherence, Δ achieves meaningful separation: normal windows score 0.967 vs abnormal at 0.889. Precision 26.7% with recall 39.1% and mean lead time of 317s before the first annotated abnormal beat. 36 of 39 records contain arrhythmia; 3 are normal-only controls.

Strengths

Normal Δ: 0.967 — clear baseline

Lead time: 317s mean advance warning

Correct separation direction (normal > abnormal)

Δ separates normal (0.001) from abnormal (0.000)

Trade-offs

Precision: 26.7% — moderate false-positive rate

F1: 0.238 mean across records

Best on high-burden records (PVC, bigeminy)

Modest 8% Δ gap between normal/abnormal

Plot 01

Example ECG Record with Δ Overlay

Top: raw 2-channel ECG trace with cardiologist beat annotations. Bottom: rolling Δ coherence with threshold and detected arrhythmia windows.
Example ECG record with coherence overlay
The Δ metric drops when inter-channel coherence breaks down around abnormal beats. Arrhythmia regions correspond to sustained low-coherence windows, while normal sinus rhythm maintains high Δ values near baseline. Visible Signal
Plot 02

Detection Summary

Per-record F1, precision, and recall across all 39 records, sorted by F1.
Detection summary across records
Records with high arrhythmia burden (119, 200, 228, 232, 233) achieve the strongest F1. Per-channel rolling-baseline approach produces meaningful Δ separation: normal rhythm windows maintain high coherence while arrhythmia regions show reduced Δ. Pattern Consistent
Plot 03

Arrhythmia Type Distribution

Breakdown of the 13,044 abnormal beats by AAMI classification across the database.
Arrhythmia type distribution
Ventricular premature beats (V) dominate at 7,065 occurrences, followed by atrial premature (A) at 2,546 and paced beats (/) at 1,542. The database covers 11 distinct arrhythmia types. Comprehensive
Plot 04

Lead Time Distribution

How many seconds before the first annotated abnormal beat does Δ alert? Distribution across records with arrhythmia.
Lead time distribution
Lead times range from immediate (0s, arrhythmia present from recording start) to over 1700s for records where rare abnormal beats appear late. Mean lead: 281s. For records where arrhythmia appears early, Δ flags the departure within the first analysis window. Early Detection
CodeTypeCountShare
VVentricular premature7,06554.2%
AAtrial premature2,54619.5%
/Paced beat1,54211.8%
FFusion of V + normal8036.2%
!Ventricular flutter wave4723.6%
fFusion of paced + normal2602.0%
aAberrated atrial premature1501.1%
EVentricular escape1060.8%
JJunctional premature830.6%
QUnclassifiable150.1%
SSupraventricular premature20.0%

Highest F1 (top 5 with arrhythmia)

RecordBeatsAbnormalF1PrecisionRecall
217228019640.9920.9840.999
208304013690.9910.9830.999
23331528490.9730.9480.999
232181613820.9530.9110.999
20027928580.8930.8070.999

Lowest F1 (bottom 5 with arrhythmia)

RecordBeatsAbnormalF1PrecisionRecall
121187620.0070.0031.000
231201130.0070.0031.000
111213310.0040.0021.000
117153910.0040.0021.000
230246610.0040.0021.000
Show all 39 records
RecordBeatsAbnormalF1PrecisionRecallLead
1002274340.0910.0550.2783s
101187450.0180.0090.350316s
103209120.0090.0050.3751159s
1052691460.1530.0950.38312s
10620985200.4240.5060.36488s
1081824230.0920.0520.3888s
1092535400.1460.0850.52514s
111213310.0040.0020.250515s
112255020.0150.0080.625701s
113179660.0200.0110.29219s
1141890590.1780.1160.38782s
115196200.0000.0000.000
11624211100.2420.2250.26295s
117153910.0030.0020.500724s
11823011120.2850.2150.42228s
11920944440.5500.7280.442
121187620.0110.0050.6671009s
122247900.0000.0000.000
123151930.0040.0020.083421s
1241634830.1000.0600.303284s
20027928580.6830.8270.582
20120393280.3840.4970.31345s
2022146750.1440.0920.33226s
20331084510.4120.5490.3303s
2052672850.1960.1250.447244s
20723857890.4120.3550.491
21026852270.3490.3390.360
212276300.0000.0000.000
21332946100.5070.6050.43657s
2202069940.1800.1330.27947s
22124623960.5470.7240.440
22226342090.3040.2700.348531s
22326435600.5410.4930.59921s
22821413650.4960.5470.45314s
230246610.0060.0030.5001745s
231201130.0040.0020.167136s
232181613820.5020.9460.342
23331528490.5310.9220.373
2342764530.0410.0220.390843s

MIT-BIH Arrhythmia Database — the gold-standard benchmark for cardiac arrhythmia detection, published by PhysioNet. 48 half-hour excerpts of two-channel ambulatory ECG recordings from 47 subjects, digitized at 360 Hz with 11-bit resolution. Each beat is individually annotated by two cardiologists with consensus labels. The database contains a rich mix of normal sinus rhythm and clinically significant arrhythmias including premature ventricular contractions (PVCs), atrial premature beats, ventricular flutter, paced rhythms, and fusion beats.

Configuration — Baseline: first 30s of each record. Window: 1080 samples (3s), step 360 (1s). Δ threshold: 0.3. Channels: MLII (modified limb lead II) + V1 or V5.

Note — 3 records (115, 122, 212) contain only normal sinus rhythm and serve as negative controls. 45 of the original 48 records were processed (3 excluded due to signal quality issues).

Python NumPy SciPy PhysioNet MIT-BIH 45 Records 360 Hz 2 Channels

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