ECG Signal Processing Projects for Final Year Students (2026)
Electrocardiogram (ECG) signal processing underpins QRS detection, arrhythmia classification, heart-rate variability (HRV) analysis and modern deep-learning diagnosis. Student projects range from classical filters to CNN/Transformer models on public databases.
This page lists 90+ high-impact ECG topics. Tools include WFDB, NeuroKit2, scipy, biosppy, PyTorch/TensorFlow and scikit-learn. Key datasets: MIT-BIH Arrhythmia, PTB-XL, MIT-BIH NSR and European ST-T. Ideal for BE, BTech, MTech biomedical and ECE students in Bangalore and across India.
ECG Signal Processing Classification
Core Frameworks & ToolsLibraries and datasets commonly used in ECG academic and clinical research projects.
WFDB
NeuroKit2
scipy / biosppy
PyTorch / TF
scikit-learn
MIT-BIH / PTB-XL
Best ECG Signal Processing Topics & Tools (90+)
Grouped by theme. Each topic lists primary tools and typical datasets.
| # | Project Topic | Primary Tools / Datasets |
|---|---|---|
| 🧹 Preprocessing · Filtering · Denoising | ||
| 1 | PreBaseline Wander Removal (high-pass / median / wavelet) | scipy, NeuroKit2, MIT-BIH |
| 2 | PrePower-Line Interference (50/60 Hz) Notch Filtering | scipy.signal, IIR notch |
| 3 | PreEMG / Muscle Noise Reduction | Band-pass, wavelet, NeuroKit2 |
| 4 | PreWavelet Denoising of ECG | PyWavelets, soft threshold |
| 5 | PreAdaptive Filtering for Motion Artifact | LMS/NLMS, wearable ECG |
| 6 | PreComparison of Filtering Pipelines (SNR / morphology) | MIT-BIH, quality metrics |
| 7 | PreECG Signal Quality Assessment (SQA) Index | NeuroKit2, custom rules |
| 8 | PreResampling and Interpolation Strategies | scipy, WFDB |
| 9 | PreLead Selection / Single-Lead Extraction Demo | 12-lead → lead II, PTB-XL |
| 10 | PreEnd-to-End Preprocess Pipeline with Report | NeuroKit2, WFDB, plots |
| 📍 QRS Detection · R-Peak · Delineation | ||
| 11 | QRSPan-Tompkins QRS Detection Implementation | Python, MIT-BIH annotations |
| 12 | QRSWavelet-Based QRS Detector | PyWavelets, MIT-BIH |
| 13 | QRSHamilton / Engelse-Zeelenberg Detector Comparison | NeuroKit2, sensitivity/PPV |
| 14 | QRSR-Peak Detection for Noisy Wearable ECG | Adaptive threshold, custom data |
| 15 | QRSQRS Delineation: Onset, Offset, QRS Duration | NeuroKit2, LUDB / QTDB |
| 16 | QRSP and T Wave Detection and Morphology Features | Delineation, MIT-BIH / QTDB |
| 17 | QRSDeep Learning QRS Detector (CNN / U-Net style) | PyTorch, MIT-BIH |
| 18 | QRSReal-Time QRS Detection Latency Study | Streaming buffer, timing |
| 19 | QRSMulti-Lead QRS Decision Fusion | 12-lead logic, PTB-XL |
| 20 | QRSDetector Robustness to Arrhythmia Morphology | MIT-BIH AAMI classes |
| 💓 Arrhythmia Classification | ||
| 21 | ArrBeat Classification: N, S, V, F (AAMI) | MIT-BIH, sklearn / CNN |
| 22 | ArrFeature-Based Arrhythmia Classifier (time/freq) | HRV + morphology, RF/SVM |
| 23 | ArrCNN for ECG Beat Classification | PyTorch, MIT-BIH |
| 24 | ArrLSTM / GRU Sequence Model for Rhythm | PyTorch, MIT-BIH segments |
| 25 | ArrPatient-Specific vs Inter-Patient Evaluation | DS1/DS2 splits, MIT-BIH |
| 26 | ArrImbalanced Learning for Rare Arrhythmias | SMOTE / class weights |
| 27 | ArrAtrial Fibrillation Detection from RR Intervals | AFDB / MIT-BIH AF, features |
| 28 | ArrVentricular Ectopy / PVC Detection Pipeline | MIT-BIH, morphology + ML |
| 29 | ArrMulti-Label Diagnosis on PTB-XL | PTB-XL, CNN / EfficientNet |
| 30 | ArrExplainable AI for Arrhythmia Decisions | Grad-CAM / SHAP on ECG |
| 📊 HRV Analysis · Autonomic Features | ||
| 31 | HRVTime-Domain HRV (SDNN, RMSSD, pNN50) | NeuroKit2, MIT-BIH NSR |
| 32 | HRVFrequency-Domain HRV (LF, HF, LF/HF) | Welch PSD, NeuroKit2 |
| 33 | HRVNonlinear HRV (Poincaré, entropy, DFA) | NeuroKit2, custom scripts |
| 34 | HRVShort-Term vs Long-Term HRV Comparison | 5-min vs 24-h style windows |
| 35 | HRVStress / Workload Proxy from HRV Features | HRV features, labeled sessions |
| 36 | HRVEctopy Correction Impact on HRV Metrics | Interpolation methods, NSR |
| 37 | HRVHRV-Based Sleep Stage / Quality Indicators | Overnight ECG, HRV trends |
| 38 | HRVReal-Time HRV Dashboard from Streaming ECG | Buffer, NeuroKit2, plotly |
| 🧠 Deep Learning on ECG | ||
| 39 | DL1D CNN for Arrhythmia Classification | PyTorch, MIT-BIH |
| 40 | DLResNet / Inception-style 1D Models for ECG | PTB-XL, PyTorch |
| 41 | DLTransformer / Attention Models for ECG | ECG Transformer, PTB-XL |
| 42 | DLTransfer Learning from Large ECG Models | Pretrained weights, fine-tune |
| 43 | DLSelf-Supervised Pretraining on Unlabeled ECG | Contrastive / masked, PTB-XL |
| 44 | DLMulti-Task Learning: Rhythm + Morphology | Shared backbone, multi-head |
| 45 | DLFew-Shot Learning for Rare Arrhythmias | Episode-based, MIT-BIH rare |
| 46 | DLDomain Adaptation: Hospital → Wearable ECG | DA methods, dual datasets |
| 47 | DLUncertainty Estimation in ECG Diagnosis | MC dropout / ensembles |
| 48 | DLModel Compression for Edge ECG Inference | Quantization, pruning, mobile |
| ⌚ Wearable · Single-Lead · Mobile ECG | ||
| 49 | WearSingle-Lead Wearable ECG Acquisition Demo | AD8232 / similar, MCU |
| 50 | WearMotion Artifact Mitigation for Wearables | Accel fusion, adaptive filter |
| 51 | WearOn-Device QRS and HR Display | Embedded C / MicroPython |
| 52 | WearBluetooth ECG Stream to Phone Dashboard | BLE, Flutter / web plot |
| 53 | WearDry vs Wet Electrode Signal Quality Study | SNR, SQA metrics |
| 54 | WearLong-Term Wearable Recording Storage Pipeline | Compression, cloud optional |
| 55 | WearPrivacy-Preserving Wearable ECG Analytics | On-device only / federated lite |
| 📈 Feature Extraction · Spectral Analysis | ||
| 56 | FeatMorphological Feature Set for Beat Classification | QRS width, amplitudes, MIT-BIH |
| 57 | FeatWavelet Coefficient Features for ECG | DWT, energy features |
| 58 | FeatST-Segment and T-Wave Morphology Analysis | European ST-T, delineation |
| 59 | FeatSpectral Analysis of ECG (PSD, spectrogram) | scipy, STFT visualization |
| 60 | FeatPCA / ICA for Multi-Lead ECG Decomposition | sklearn, 12-lead PTB |
| 🏥 Clinical · Domain Applications | ||
| 61 | ClinMyocardial Infarction Indicators from ECG | PTB / PTB-XL, features / CNN |
| 62 | ClinIschemia Detection from ST Changes | European ST-T database |
| 63 | ClinHeart Rate Zone and Exercise ECG Analysis | Stress-test style recordings |
| 64 | ClinPediatric vs Adult ECG Parameter Differences | Age-group datasets if available |
| 65 | ClinTelemedicine ECG Screening Pipeline | Upload → preprocess → classify |
| 66 | ClinAlarm Fatigue Reduction: Priority Scoring | False alarm reduction rules |
| 🔬 Advanced · Standards · Research | ||
| 67 | AdvInter-Patient Generalization Benchmark | MIT-BIH DS1/DS2, metrics |
| 68 | AdvCross-Database Evaluation (MIT-BIH ↔ PTB-XL) | Domain shift study |
| 69 | AdvSynthetic ECG Generation (GANs / diffusion lite) | Augmentation, privacy |
| 70 | AdvAdversarial Robustness of ECG Classifiers | Perturbations, defense |
| 71 | AdvFederated Learning for Multi-Hospital ECG | Flower / custom, privacy |
| 72 | AdvContinual Learning on Streaming ECG | CL methods, sequential tasks |
| 73 | AdvMultimodal: ECG + PPG Joint Analysis | Aligned recordings, fusion |
| 74 | AdvECG Biometrics / Subject Identification | Template matching / CNN |
| 75 | AdvQT Interval Measurement Automation | QTDB, delineation accuracy |
| 76 | AdvBundle Branch Block Morphology Detection | MIT-BIH / PTB-XL labels |
| 77 | AdvReal-Time Embedded ECG Pipeline Benchmark | MCU / RPi, latency/power |
| 78 | AdvAnnotation Quality and Label Noise Study | Inter-annotator concepts |
| 79 | AdvOpen-Source ECG Toolchain Reproducibility | WFDB + NeuroKit2 report |
| 80 | AdvEnd-to-End: Acquire → Filter → Detect → Classify → Report | Full pipeline demo |
| 81 | AdvGender / Age Bias Analysis in ECG Models | PTB-XL demographics |
| 82 | AdvCalibration of Probability Scores for Diagnosis | Reliability diagrams |
| 83 | AdvActive Learning for Efficient ECG Annotation | Uncertainty sampling |
| 84 | AdvECG Signal Compression for Storage / Telemetry | Wavelet / residual coding |
| 85 | AdvStandards Mapping: IEC / AAMI Performance Claims | Sensitivity, PPV documentation |
| 86 | AdvComparative Study: Classical vs DL Arrhythmia | Same splits, metrics table |
| 87 | AdvNoise Stress Test: Detector under SNR Sweep | Synthetic noise, MIT-BIH |
| 88 | AdvLead-I / Smartwatch ECG Feasibility Study | Single-lead limits analysis |
| 89 | AdvEducational Lab Kit: From Filter to Classifier | Curriculum + Jupyter notebooks |
| 90 | AdvFull Research Pipeline: Data → Model → Clinical Metrics → Report | End-to-end thesis-style |
| 91 | AdvOpen Dataset Curation and License Compliance Notes | MIT-BIH, PTB-XL usage |
| 92 | AdvReproducible Benchmark Suite for ECG Classifiers | Fixed seeds, public splits |
Topics reflect biomedical signal processing and clinical ECG research practice. Contact us for pipeline scripts, evaluation metrics, university-format report, PPT and viva Q&A for any topic above.
Why Choose Us for ECG Projects?
Bangalore-based guidance for BE, BTech and MTech students working on biomedical ECG signal processing.
Preprocessing & QRS
Filtering, baseline removal and classical/deep QRS detectors with MIT-BIH evaluation.
Arrhythmia Classification
AAMI beat classes, AF detection and PTB-XL multi-label diagnosis with classical and DL models.
HRV Analysis
Time, frequency and nonlinear HRV features with NeuroKit2 and clear physiological interpretation.
Wearable & Deep Learning
Single-lead wearable pipelines and CNN/Transformer models with edge deployment options.
Frequently Asked Questions — ECG Projects
Top topics include baseline and noise filtering, Pan-Tompkins and wavelet QRS detection, arrhythmia classification on MIT-BIH, HRV analysis, deep learning on PTB-XL, and wearable single-lead ECG pipelines.
WFDB, NeuroKit2, scipy/signal, biosppy, PyTorch/TensorFlow, scikit-learn; datasets include MIT-BIH Arrhythmia, PTB-XL, MIT-BIH NSR and European ST-T.
Yes. Packages include preprocessing scripts, detection/classification pipelines, evaluation metrics, university-format report, PPT and viva Q&A.
Single-lead (e.g. lead II or wearable) is common for QRS, HRV and basic arrhythmia screening. Multi-lead (12-lead) enables richer morphology, localization of ischemia and multi-label diagnosis as in PTB-XL.
ECG Signal Processing Lab — Bangalore
Signal analysis workstations, public databases and wearable ECG demos for BE, BTech and MTech biomedical projects.
Filtering &
Denoising
Baseline / NotchDenoising
Pan-Tompkins
QRS Detection
MIT-BIHQRS Detection
Arrhythmia
Classification
AAMI ClassesClassification
HRV Time /
Frequency
NeuroKit2Frequency
Deep Learning
PTB-XL
CNN / TransformerPTB-XL
Wearable ECG
Acquisition
Single-LeadAcquisition
Explainability
Grad-CAM
Clinical TrustGrad-CAM
Viva & Report
Preparation
DocumentationPreparation