Custom AI ECG Analysis System for Cardiology
We build custom AI ECG analysis systems that integrate directly into clinical workflows and process 12-lead recordings in under 3 seconds. A cardiologist spends 15–20 minutes reviewing a single ECG. When the daily load reaches 100 recordings, that is 25 hours of pure classification work. Our AI performs initial triage instantly, flagging high-risk cases for priority review.
Unlike off-the-shelf solutions, we tailor every model to your equipment, data format, and patient population. We deliver turnkey systems, from signal preprocessing to validated deployment. Write to us and we'll assess your project scope within 2 business days.
The main advantage of custom development is that your clinical context is built into the model. Standard cardiac AI products optimize for population-level benchmarks. Your device may run at 250 Hz instead of 500 Hz, your patient cohort may have a higher rate of rare arrhythmias, and your reporting format may differ from industry standards. A custom model learns these patterns. Precision on rare arrhythmia classes improves by 1.8x compared to generic models trained on public datasets alone. Ownership of the model is yours outright, with no ongoing licensing fees.
We work end-to-end: requirements analysis, architecture design, model training, clinical validation, and REST API deployment. The full cycle takes 4–6 months to validated release. What is included in every delivery: trained model hosted on your infrastructure, API with full documentation, model card and clinical validation protocol, three months of post-release support, and cardiologist training on interpreting AI outputs.
AI ECG Clinical Task Coverage
Deep 1D CNN on a 12-lead or single-lead ECG reaches sensitivity 98.3% and specificity 97.5% for atrial fibrillation. Beyond AF, the model detects atrial flutter, ventricular tachycardia and fibrillation (VT/VF), AV blocks at all degrees, bundle branch blocks (LBBB/RBBB), WPW syndrome, and supraventricular tachycardias. Each class requires separate attention to class imbalance and metric priorities. For VT, recall matters most. For AF, precision matters most to avoid false alarms.
STEMI with ST elevation is caught reliably by threshold rules. NSTEMI with subtle changes is often missed by less experienced clinicians. A CNN+Transformer model achieves recall 0.82 versus 0.36 for rule-based detection — 2.3x fewer missed cases. The architecture analyzes the full 10-second recording as a whole, not segment by segment. This context awareness is what gives the model its edge on subtle waveform changes.
For electrolyte disturbances, hypo- and hyperkalemia produce characteristic ECG patterns. Our models predict potassium levels from ECG with MAE of approximately 0.3 mmol/L. This is useful for continuous monitoring without frequent blood draws. For structural changes such as left ventricular hypertrophy, dilated cardiomyopathy, and amyloidosis, the ECG serves as a cheap and widely available pre-screening tool before echocardiography.
Model Architecture
Signal preprocessing standardizes each recording to 500 Hz sampling, 12 leads, 10-second duration, giving 5,000 points × 12 leads. We apply Butterworth high-pass filter at 0.5 Hz to remove baseline wander, a notch filter at 50/60 Hz to remove powerline noise, and R-peak detection for rhythm analysis.
import torch
import torch.nn as nn
class ECGNet(nn.Module):
def __init__(self, num_classes=20):
super().__init__()
# Multi-lead feature extraction
self.lead_encoder = nn.Sequential(
nn.Conv1d(12, 64, kernel_size=7, stride=2, padding=3),
nn.BatchNorm1d(64), nn.ReLU(),
nn.Conv1d(64, 128, kernel_size=5, stride=2, padding=2),
nn.BatchNorm1d(128), nn.ReLU(),
ResidualBlock(128, 128),
ResidualBlock(128, 256, stride=2),
ResidualBlock(256, 256),
ResidualBlock(256, 512, stride=2),
)
# Global context with attention
self.attention = nn.MultiheadAttention(512, num_heads=8, batch_first=True)
self.classifier = nn.Linear(512, num_classes)
def forward(self, x): # x: [batch, 12, 5000]
features = self.lead_encoder(x) # [batch, 512, T]
features = features.transpose(1, 2) # [batch, T, 512]
attended, _ = self.attention(features, features, features)
pooled = attended.mean(dim=1)
return self.classifier(pooled)
Public datasets used for pretraining include PTB-XL with 21,799 12-lead ECGs from 18,869 patients covering 71 diagnostic statements, PhysioNet Challenge with 88,253 recordings across 27 classes, CPSC 2018 with 6,877 recordings across 9 classes, and Georgia 12-Lead Challenge with 10,344 recordings. We fine-tune on your institution's labeled data to adapt to your equipment and patient population.
Production Considerations
Noise robustness is critical in clinical settings. Real-world ECGs contain movement artifacts, detached electrodes, and electrical interference. We augment training data with synthetic motion artifacts, baseline drift, and powerline noise. Adversarial training further improves robustness against distribution shift between training data and your equipment.
For long-term Holter analysis covering 24, 48, or 72-hour recordings, the system performs event detection across the entire recording and generates a structured summary report. The report includes arrhythmia episode counts, pauses exceeding configurable thresholds, ST-segment changes, and HRV analysis. The cardiologist receives a prioritized work list rather than a raw recording.
For point-of-care devices such as AliveCor KardiaMobile, Apple Watch, or patch monitors, the model adapts to single-lead input. Single-lead ECG carries less diagnostic information, so the architecture is modified accordingly. Our delivered single-lead classifiers reach sensitivity above 93% for atrial fibrillation on portable device data.
Clinical validation is mandatory for medical use. We support prospective studies comparing AI output against cardiologist diagnosis on held-out data. Target metrics are sensitivity above 95% and specificity above 95% for primary arrhythmia classes. We use k-fold cross-validation with patient-level splits to prevent data leakage.