We propose ENCASE to combine expert features and DNNs (Deep Neural Networks) together for ECG classification. We first explore and implement expert features from statistical area, signal processing area and medical area. Then, we build DNNs to automatically extract deep features. Besides, we propose a new algorithm to find the most representative wave (called centerwave) among long ECG record, and extract features from centerwave. Finally, we combine these features together and put them into ensemble classifiers. Experiment on 4-class ECG data classification reports 0.84 F 1 score, which is much better than any of the single model.

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Objective: We aim to combine deep neural networks and engineered features (hand-crafted features based on medical domain knowledge) for cardiac arrhythmia detection from short single-lead ECG recordings. Approach: We propose a two-stage method named for cardiac arrhythmia detection. The first stage is feature extraction and the second stage is classifier building. In the feature extraction stage, we extract both deep features and engineered features. Deep features are obtained by modifying deep neural networks into a deep feature extractor. Engineered features are extracted by summarizing existing approaches into four feature groups. Then, we propose a feature aggregation approach to combine these features. In the classifier building stage, we build multiple gradient boosting decision trees and combine them to get the final detector. Main results: Experiments are performed on the PhysioNet/Computing in Cardiology Challenge 2017 dataset (Clifford et al 2017 Computing in Cardiology vol 44). Using F1 scores reported on the hidden test set as measurements, got 0.9117 on Normal (F1N), 0.8128 on Atrial Fibrillation (AF) (F1A), 0.7505 on Others (F1O), and 0.5671 on Noise (F1P). It placed 5th in the Challenge and 8th in the follow-up challenge (ranked by considering the average of Normal, AF, and Others (F1NAO  =  0.825)). When rounding to two decimal places, we were part a three-way tie for 1st place and were part a seven-way tie for 2nd place in the follow-up challenge. Further experiments show that combined features perform better than individual features, and deep features show more importance scores than other features. Significance: can benefit from both feature engineering-based methods and recent deep neural networks. It is flexible and can easily assimilate the ability of new cardiac arrhythmia detection methods.

Multi-resonant wideband energy harvester based on a folded asymmetric M-shaped cantileverThis article reports a compact wideband piezoelectric vibration energy harvester consisting of three proof masses and an asymmetric M-shaped cantilever. The M-shaped beam comprises a main beam and two folded and dimension varied auxiliary beams interconnected through the proof mass at the end of the main cantilever. Such an arrangement constitutes a three degree-of-freedom vibrating body, which can tune the resonant frequencies of its first three orders close enough to obtain a utility wide bandwidth. The finite element simulation results and the experimental results are well matched. The operation bandwidth comprises three adjacent voltage peaks on account of the frequency interval shortening mechanism. The result shows that the proposed piezoelectric energy harvester could be efficient and adaptive in practical vibration circumstance based on multiple resonant modes. C