An arrhythmia classification algorithm using a dedicated wavelet adapted to different subjects

<p>Abstract</p> <p>Background</p> <p>Numerous studies have been conducted regarding a heartbeat classification algorithm over the past several decades. However, many algorithms have also been studied to acquire robust performance, as biosignals have a large amount of variation among individuals. Various methods have been proposed to reduce the differences coming from personal characteristics, but these expand the differences caused by arrhythmia.</p> <p>Methods</p> <p>In this paper, an arrhythmia classification algorithm using a dedicated wavelet adapted to individual subjects is proposed. We reduced the performance variation using dedicated wavelets, as in the ECG morphologies of the subjects. The proposed algorithm utilizes morphological filtering and a continuous wavelet transform with a dedicated wavelet. A principal component analysis and linear discriminant analysis were utilized to compress the morphological data transformed by the dedicated wavelets. An extreme learning machine was used as a classifier in the proposed algorithm.</p> <p>Results</p> <p>A performance evaluation was conducted with the MIT-BIH arrhythmia database. The results showed a high sensitivity of 97.51%, specificity of 85.07%, accuracy of 97.94%, and a positive predictive value of 97.26%.</p> <p>Conclusions</p> <p>The proposed algorithm achieves better accuracy than other state-of-the-art algorithms with no intrasubject between the training and evaluation datasets. And it significantly reduces the amount of intervention needed by physicians.</p

Robust algorithm for arrhythmia classification in ECG using extreme learning machine

<p>Abstract</p> <p>Background</p> <p>Recently, extensive studies have been carried out on arrhythmia classification algorithms using artificial intelligence pattern recognition methods such as neural network. To improve practicality, many studies have focused on learning speed and the accuracy of neural networks. However, algorithms based on neural networks still have some problems concerning practical application, such as slow learning speeds and unstable performance caused by local minima.</p> <p>Methods</p> <p>In this paper we propose a novel arrhythmia classification algorithm which has a fast learning speed and high accuracy, and uses Morphology Filtering, Principal Component Analysis and Extreme Learning Machine (ELM). The proposed algorithm can classify six beat types: normal beat, left bundle branch block, right bundle branch block, premature ventricular contraction, atrial premature beat, and paced beat.</p> <p>Results</p> <p>The experimental results of the entire MIT-BIH arrhythmia database demonstrate that the performances of the proposed algorithm are 98.00% in terms of average sensitivity, 97.95% in terms of average specificity, and 98.72% in terms of average accuracy. These accuracy levels are higher than or comparable with those of existing methods. We make a comparative study of algorithm using an ELM, back propagation neural network (BPNN), radial basis function network (RBFN), or support vector machine (SVM). Concerning the aspect of learning time, the proposed algorithm using ELM is about 290, 70, and 3 times faster than an algorithm using a BPNN, RBFN, and SVM, respectively.</p> <p>Conclusion</p> <p>The proposed algorithm shows effective accuracy performance with a short learning time. In addition we ascertained the robustness of the proposed algorithm by evaluating the entire MIT-BIH arrhythmia database.</p

Synthesis of metastable Ruddlesden–Popper titanates, (ATiO3)nAO, with n ≥ 20 by molecular-beam epitaxy

We outline a method to synthesize (ATiO3)nAO Ruddlesden–Popper phases with high-n, where the A-site is a mixture of barium and strontium, by molecular-beam epitaxy. The precision and consistency of the method described is demonstrated by the growth of an unprecedented (SrTiO3)50SrO epitaxial film. We proceed to investigate barium incorporation into the Ruddlesden–Popper structure, which is limited to a few percent in bulk, and we find that the amount of barium that can be incorporated depends on both the substrate temperature and the strain state of the film. At the optimal growth temperature, we demonstrate that as much as 33% barium can homogeneously populate the A-site when films are grown on SrTiO3 (001) substrates, whereas up to 60% barium can be accommodated in films grown on TbScO3 (110) substrates, which we attribute to the difference in strain. This detailed synthetic study of high n, metastable Ruddlesden–Popper phases is pertinent to a variety of fields from quantum materials to tunable dielectric

Synthesis of Metastable Ruddlesden–Popper Titanates, (\u3cem\u3eA\u3c/em\u3eTiO\u3csub\u3e3\u3c/sub\u3e)\u3csub\u3e\u3cem\u3en\u3c/em\u3e\u3c/sub\u3e\u3cem\u3eA\u3c/em\u3eO, with \u3cem\u3en\u3c/em\u3e ≥ 20 by Molecular-Beam Epitaxy

We outline a method to synthesize (ATiO3)nAO Ruddlesden–Popper phases with high-n, where the A-site is a mixture of barium and strontium, by molecular-beam epitaxy. The precision and consistency of the method described is demonstrated by the growth of an unprecedented (SrTiO3)50SrO epitaxial film. We proceed to investigate barium incorporation into the Ruddlesden–Popper structure, which is limited to a few percent in bulk, and we find that the amount of barium that can be incorporated depends on both the substrate temperature and the strain state of the film. At the optimal growth temperature, we demonstrate that as much as 33% barium can homogeneously populate the A-site when films are grown on SrTiO3 (001) substrates, whereas up to 60% barium can be accommodated in films grown on TbScO3 (110) substrates, which we attribute to the difference in strain. This detailed synthetic study of high n, metastable Ruddlesden–Popper phases is pertinent to a variety of fields from quantum materials to tunable dielectrics

Enhanced Switching Pattern to Improve Cell Balancing Performance in Active Cell Balancing Circuit using Multi-winding Transformer

Cell balancing performance is an important factor in determining the operational efficiency of the active cell balancing circuit. Thus, this study approached this need by developing an enhanced switching pattern. The circuit is designed to transfer energy between arbitrary source and target cells. It has been operated in flyback and buck-boost modes according to the position of the source and target cells. In this circuit, the coupling coefficient of the transformer considerably affects the balancing performance of the flyback operation. The energy transferred to the non-target cell is increased by the low-coupling coefficient due to the leakage inductance. Therefore, the high energy transfer ratio cannot be achieved using conventional switching patterns. In this paper, a new flyback switching pattern is proposed, which can minimize the effect of the coupling coefficient in the cell balancing operation. The proposed switching pattern uses the cells which do not participate in the balancing process to control the voltage applied to each winding, which results in a high energy transfer ratio irrespective of the coupling coefficient. In addition, an enhanced operating method has been proposed to improve the cell balancing speed by reducing the energy transfer path in specific cell conditions. The performance of the proposed switching pattern was verified in a 15 W cell balancing circuit