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.

Measuring body temperature is considerably important to physiological studies as well as clinical investigations. In recent years, numerous observations have been reported and various methods of measurement have been employed. The present paper introduces a novel wearable sensor in intelligent clothing for human body temperature measurement. The objective is the integration of optical fiber Bragg grating (FBG)-based sensors into functional textiles to extend the capabilities of wearable solutions for body temperature monitoring. In addition, the temperature sensitivity is 150 pm/°C, which is almost 15 times higher than that of a bare FBG. This study combines large and small pipes during fabrication to implant FBG sensors into the fabric. The law of energy conservation of the human body is considered in determining heat transfer between the body and its clothing. The mathematical model of heat transmission between the body and clothed FBG sensors is studied, and the steady-state thermal analysis is presented. The simulation results show the capability of the material to correct the actual body temperature. Based on the skin temperature obtained by the weighted average method, this paper presents the five points weighted coefficients model using both sides of the chest, armpits, and the upper back for the intelligent clothing. The weighted coefficients of 0.0826 for the left chest, 0.3706 for the left armpit, 0.3706 for the right armpit, 0.0936 for the upper back, and 0.0826 for the right chest were obtained using Cramer’s Rule. Using the weighting coefficient, the deviation of the experimental result was ± 0.18°C, which favors the use for clinical armpit temperature monitoring. Moreover, in special cases when several FBG sensors are broken, the weighted coefficients of the other sensors could be changed to obtain accurate body temperature.

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.

Silicon photonic integration is a means to produce an integrated on-chip fiber Bragg grating (FBG) interrogator. The possibility of integrating the light source, couplers, grating couplers, de-multiplexers, photodetectors (PDs), and other optical elements of the FBG interrogator into one chip may result in game-changing performance advances, considerable energy savings, and significant cost reductions. To the best of our knowledge, this paper is the first to present a hybrid silicon photonic chip based on III–V/silicon-on-insulator photonic integration for an FBG interrogator. The hybrid silicon photonic chip consists of a multiwavelength vertical-cavity surface-emitting laser array and input grating couplers, a multimode interference coupler, an arrayed waveguide grating, output grating couplers, and a PD array. The chip can serve as an FBG interrogator on a chip and offer unprecedented opportunities. With a footprint of $5\mathrm{mm}\times 3\mathrm{mm}$, the proposed hybrid silicon photonic chip achieves an interrogation wavelength resolution of approximately 1 pm and a wavelength accuracy of about $\pm 10\mathrm{pm}$. With the measured 1 pm wavelength resolution, the temperature measurement resolution of the proposed chip is approximately 0.1°C. The proposed hybrid silicon photonic chip possesses advantages in terms of cost, manufacturability, miniaturization, and performance. The chip supports applications that require extreme miniaturization down to the level of smart grains.