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Although radio frequency ablation is the most effective treatment for atrial fibrillation (AF), there is still a high recurrence rate. The purpose of this paper was to initially assess the probability of the recurrence of AF based on the preoperative body surface potential mapping (BSPM) signals, in other words, to predict the efficiency of ablation and assist physicians in developing more effective treatment options. At present, deep learning methods based on convolutional neural networks (CNNs) do not require complex mathematical abstractions or manual interventions; thus, higher computation efficiency can be obtained in such research. However, the use of the fully connected multi-layer perceptron (MLP) algorithms has shown low classification performance. This paper proposes an improved CNN algorithm (CNN-SVM method) for the recurrence classification in AF patients by combining with the support vector machine (SVM) architecture. The algorithm is validated on the preoperative AF signals of 14 patients for classification. All postoperative patients are followed up for one year; ten of them remain in sinus rhythm, whereas the other four turn back to AF. The ECG data for these patients are obtained through the 128-Lead BSPM system. The results show that the proposed CNN-SVM method can automatically extract the characteristic information through the CNN network. The constructed model ultimately achieved an accuracy of 96%, a sensitivity of 88%, and a specificity of 96%. It is concluded that the CNN-SVM method solves the drawbacks of MLP only for separating linear data. It improves the overall performance of AF recurrence classification, thereby providing a valuable reference for doctors to develop personalized treatment plans. INDEX TERMS Classification of AF recurrence, deep learning, body surface potential mapping, support vector machines. The associate editor coordinating the review of this manuscript and approving it for publication was Yonghong Peng.

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proposed to harvest the ultrasonic wave's energy using zinc oxide (ZnO) nanowire arrays, [9] the nanogenerator has entered a period of rapid development. [10][11][12][13] Various energies have been harvested using many kinds of nanogenerators, [4] such as triboelectric nanogenerators, [13] PENGs, [14] thermal-electric nanogenerators, [15] and photoelectric nanogenerators. [16] In addition, the as-harvested energy has various resources, such as wind, [17][18][19] heat energy, [20,21] solar power, [22] vibration, [23,24] mechanical energy, [25] electromagnetic waves, [26] chemical energy, [27] and water energy. [11,28,29] Therein, the nanogengerator induced from water-flow [30][31][32][33][34] and water evaporation [29,35] is a majority part of the water energy nanogenerator. Generally, the energy-produced principle from water-flow and water evaporation could be divided into two categories according to the surface properties of the nanogenerator. On the one hand, as the ionic solution

Of late, many nucleic acid analysis platforms have been established, but there is still room for constructing integrated nucleic acid detection systems with high nucleic acid extraction efficiency, low detection cost, and convenient operation. In this work, a simple rotary valve-assisted fluidic chip coupling with CRISPR/Cas12a was established to achieve fully integrated nucleic acid detection. All of the detection reagents were prestored on the fluidic chip. With the aid of the rotary valve and syringe, the liquid flow and stirring can be precisely controlled. The nucleic acid extraction, loop-mediated isothermal amplification (LAMP) reaction, and CRISPR detection could be completed in 80 min. A clean reservoir and an air reservoir on the fluidic chip were designed to effectively remove the remaining ethanol. With Vibrio parahaemolyticus as the targets, the detection sensitivity of the fluidic chip could reach 3.1 × 101 copies of target DNA per reaction. A positive sample could be sensitively detected by CRISPR/Cas12a to produce a green fluorescent signal, while a negative sample generated no fluorescent signal. Further, the fluidic chip was successfully applied for detection of spiked shrimp samples, which showed the same detection sensitivity. A great feasibility for real-sample detection was showed by the fluidic chip. The proposed detection platform did not need expensive centrifugal instruments or pumps, which displayed its potential to become a powerful tool for food safety analysis and clinical diagnostics, especially in the resource-limited areas.

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