Feature extraction and classification of electrocardiogram (ECG) signals are necessary for the automatic diagnosis of cardiac diseases. In this study, a novel method based on genetic algorithm-back propagation neural network (GA-BPNN) for classifying ECG signals with feature extraction using wavelet packet decomposition (WPD) is proposed. WPD combined with the statistical method is utilized to extract the effective features of ECG signals. The statistical features of the wavelet packet coefficients are calculated as the feature sets. GA is employed to decrease the dimensions of the feature sets and to optimize the weights and biases of the back propagation neural network (BPNN). Thereafter, the optimized BPNN classifier is applied to classify six types of ECG signals. In addition, an experimental platform is constructed for ECG signal acquisition to supply the ECG data for verifying the effectiveness of the proposed method. The GA-BPNN method with the MIT-BIH arrhythmia database achieved a dimension reduction of nearly 50% and produced good classification results with an accuracy of 97.78%. The experimental results based on the established acquisition platform indicated that the GA-BPNN method achieved a high classification accuracy of 99.33% and could be efficiently applied in the automatic identification of cardiac arrhythmias.

Automatic recognition of arrhythmias is particularly important in the diagnosis of heart diseases. This study presents an electrocardiogram (ECG) recognition system based on multi-domain feature extraction to classify ECG beats. An improved wavelet threshold method for ECG signal pre-processing is applied to remove noise interference. A novel multi-domain feature extraction method is proposed; this method employs kernel-independent component analysis in nonlinear feature extraction and uses discrete wavelet transform to extract frequency domain features. The proposed system utilises a support vector machine classifier optimized with a genetic algorithm to recognize different types of heartbeats. An ECG acquisition experimental platform, in which ECG beats are collected as ECG data for classification, is constructed to demonstrate the effectiveness of the system in ECG beat classification. The presented system, when applied to the MIT-BIH arrhythmia database, achieves a high classification accuracy of 98.8%. Experimental results based on the ECG acquisition experimental platform show that the system obtains a satisfactory classification accuracy of 97.3% and is able to classify ECG beats efficiently for the automatic identification of cardiac arrhythmias.

We propose a fiber Bragg grating (FBG) sensor interrogation system utilizing a III-V vertical cavity surface emitting laser (VCSEL) as the on-chip light source. Binary blazed grating (BBG) for coupling between III-V VCSEL and silicon-on-insulator (SOI) waveguides is demonstrated for interrogation of the FBG sensor. The footprint size of the BBG is only 5.62  μm×5.3  μm, and each BBG coupler period has two subperiods. The diameter of the VCSEL's emitting window is 5 μm, which is slightly smaller than that of the BBG coupler, to be well-matched with the proposed structure. Results show that the coupling efficiency from vertical cavities of the III-V VCSEL to the in-plane waveguides reached as high as 32.6% when coupling the 1550.65 nm light. The heterogeneous integration of the III-V VCSEL and SOI waveguides by BBG plays a fundamental role in inducing a great breakthrough to the miniaturization of an on-chip light source for optical fiber sensing.