Deep Learning in Health Informatics

Transcription

1 Deep Learning in Health Informatics Rahman Peimankar Technical University of Denmark Department of Electrical Engineering (Division of Biomedical Engineering) Rahman Peimankar REAFEL Project Case Study November 13, / 15

2 An Overview of Neural Networks Development (a) Artificial neuron 1 (b) Non-linearity problem (c) Shallow vs Deep Neural Networks 2 1 url: 2 url: Rahman Peimankar REAFEL Project Case Study November 13, / 15

3 Impact of Deep Learning in Health Informatics Number of Publications Year Rahman Peimankar REAFEL Project Case Study November 13, / 15

4 Apply Deep Learning in Biomedical Engineering (d) Human health monitoring 3 (e) Power transformer condition monitoring 4 3 url: 4 url: Rahman Peimankar REAFEL Project Case Study November 13, / 15

5 REAFEL Collaboration Partners REAFEL REAching the Frail Elderly Patient for Optimizing Diagnosis of Atrial Fibrillation Rahman Peimankar REAFEL Project Case Study November 13, / 15

6 How Does C3 Work? ECG analysis (Smart Rhythm) Rahman Peimankar REAFEL Project Case Study November 13, / 15

7 Atrial Fibrillation (AFIB) (a) Electrocardiogram (ECG) (b) Normal vs. AFIB Rahman Peimankar REAFEL Project Case Study November 13, / 15

8 Generic AFIB Detection Framework 5 5 Rasmus S. Andersen, Abdolrahman Peimankar, and Sadasivan Puthusserypady. A deep learning approach for real-time detection of atrial fibrillation. In: Expert Systems with Applications 115 (2019), pp issn: Rahman Peimankar REAFEL Project Case Study November 13, / 15

9 Classification Accuracy (a) Training (Acc 97%) (b) Test (Acc 87%) BUT, this algorithm can be further improved: The training dataset is imbalanced. The P-wave absence is not investigated. The length of the episodes are quite long (30 beats per segment). Rahman Peimankar REAFEL Project Case Study November 13, / 15

10 Making the Dataset Balanced density SD_RRI Mean_RRI Class AF N density (a) Imbalanced density SD_RRI Mean_RRI Class AF N density (b) Balanced The use of the imbalanced dataset increases the chance of a biased classification, which in turns leads to a higher error rate on the minority class. Classes NSR AF Number of beats (imbalanced) Number of beats (balanced) Rahman Peimankar REAFEL Project Case Study November 13, / 15

11 Classification Accuracy Using Balanced Dataset 6 Reference: atrial fibrillation (AF) Prediction: atrial fibrillation (AF) Prediction: normal (N) (a) Training (Acc 98%) Reference: normal (N) Reference: atrial fibrillation (AF) Prediction: atrial fibrillation (AF) Prediction: normal (N) (b) Test (Acc 92%) Reference: normal (N) Table: Comparison of the classification accuracy for different number of beats per segment. Segment length L=10 L=30 L=60 Accuracy (%) Abdolrahman Peimankar and Sadasivan Puthusserypady. Ensemble Learning for Detection of Short Episodes of Atrial Fibrillation. In: th European Signal Processing Conference. Sept. 2018, pp Rahman Peimankar REAFEL Project Case Study November 13, / 15

12 P-wave Detection Using Deep Recurrent Neural Networks 7 Accuracy Ensemble BiLSTM CE BiLSTM SSE LSTM CE LSTM SSE Cutoff Cutoff = (a) Accuracy at different cutoffs Reference: P wave (P) Reference: P wave (P) Prediction: P wave (P) Prediction: No P wave (non_p) (b) Training Prediction: P wave (P) Prediction: No P wave (non_p) (c) Test 7 Abdolrahman Peimankar and Sadasivan Puthusserypady. An ensemble of deep recurrent neural networks for P-wave detection in electrocardiogram. In: submitted to 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).. Rahman Peimankar REAFEL Project Case Study November 13, / 15 Reference: No P wave (non_p) Reference: No P wave (non_p)

13 Future Work Implement the P-wave detection model into the AFIB classification algorithm. Test the model on the annotated ECG dataset from C3. Investigate the possibility of diagnosing other types of arrhythmias. Rahman Peimankar REAFEL Project Case Study November 13, / 15

14 Acknowledgment 1 Sadasivan Puthusserypady 2 Innovation Fund Denmark 3 REAFEL project collaborators: Jakob Eyvind Bardram, Maria Helena Dominguez Vall-Lamora, Anne Frølich, Christian Toft & Jacob Eric Nielsen Rahman Peimankar REAFEL Project Case Study November 13, / 15

15 Thank you for your attention! Your comments are most welcome Rahman Peimankar REAFEL Project Case Study November 13, / 15