In this paper, a novel algorithm based on a Convolutional Neural Network (CNN) is proposed for Myocardial Infarction (MI) detection via multilead electrocardiogram (ECG). A beat segmentation algorithm utilizing multilead ECG is designed to obtain multilead beats, and Fuzzy Information Granulation (FIG) is adopted for preprocessing. Then the beats are input into our Multilead-CNN (ML-CNN), a novel model that includes sub 2-D convolutional layers and Lead Asymmetric Pooling (LAP) layers. As different leads represent various angles of the same heart, LAP can capture multiscale features of different leads, exploiting the individual characteristics of each lead. In addition, sub 2-D convolution can utilize the holistic characters of all the leads. It uses 1-D kernels shared among the different leads to generate local optimal features. These strategies make the ML-CNN suitable for multilead ECG processing. To evaluate our algorithm, actual ECG datasets from the PTB diagnostic database are used. The sensitivity of our algorithm is 95.40%, the specificity is 97.37%, and the accuracy is 96.00% in the experiments. Targeting lightweight mobile healthcare applications, real-time analyses are performed on both MATLAB and ARM Cortex-A9 platforms. The average processing times for each heartbeat are approximately 17.10 ms and 26.75 ms, respectively, which indicates that this method has good potential for mobile healthcare applications.

This paper proposes a novel hybrid network named multiple-feature-branch convolutional bidirectional recurrent neural network (MFB-CBRNN) for myocardial infarction (MI) detection using 12-lead ECGs. The model efficiently combines convolutional neural network-based and recurrent neural network-based structures. Each feature branch consists of several one-dimensional convolutional and pooling layers, corresponding to a certain lead. All the feature branches are independent from each other, which are utilized to learn the diverse features from different leads. Moreover, a bidirectional long short term memory network is employed to summarize all the feature branches. Its good ability of feature aggregation has been proved by the experiments. Furthermore, the paper develops a novel optimization method, lead random mask (LRM), to alleviate overfitting and implement an implicit ensemble like dropout. The model with LRM can achieve a more accurate MI detection. Class-based and subject-based fivefold cross validations are both carried out using Physikalisch-Technische Bundesanstalt diagnostic database. Totally, there are 148 MI and 52 healthy control subjects involved in the experiments. The MFB-CBRNN achieves an overall accuracy of 99.90% in class-based experiments, and an overall accuracy of 93.08% in subject-based experiments. Compared with other related studies, our algorithm achieves a comparable or even better result on MI detection. Therefore, the MFB-CBRNN has a good generalization capacity and is suitable for MI detection using 12-lead ECGs. It has a potential to assist the real-world MI diagnostics and reduce the burden of cardiologists.

The tight-binding (TB) method is an ideal candidate for determining electronic and transport properties for a large-scale system. It describes the system as real-space Hamiltonian matrices expressed on a manageable number of parameters, leading to substantially lower computational costs than the ab-initio methods. Since the whole system is defined by the parameterization scheme, the choice of the TB parameters decides the reliability of the TB calculations. The typical empirical TB method uses the TB parameters directly from the existing parameter sets, which hardly reproduces the desired electronic structures quantitatively without specific optimizations. It is thus not suitable for quantitative studies like the transport property calculations. The ab-initio TB method derives the TB parameters from the ab-initio results through the transformation of basis functions, which achieves much higher numerical accuracy. However, it assumes prior knowledge of the basis and may encompass truncation error. Here, a machine learning method for TB Hamiltonian parameterization is proposed, within which a neural network (NN) is introduced with its neurons acting as the TB matrix elements. This method can construct the empirical TB model that reproduces the given ab-initio energy bands with predefined accuracy, which provides a fast and convenient way for TB model construction and gives insights into machine learning applications in physical problems.

Lightning waveform plays an important role in lightning observation, location, and lightning disaster investigation. Based on a large amount of lightning waveform data provided by existing real-time very low frequency/low frequency (VLF/LF) lightning waveform acquisition equipment, an automatic and accurate lightning waveform classification method becomes extremely important. With the widespread application of deep learning in image and speech recognition, it becomes possible to use deep learning to classify lightning waveforms. In this study, 50,000 lightning waveform samples were collected. The data was divided into the following categories: positive cloud ground flash, negative cloud ground flash, cloud ground flash with ionosphere reflection signal, positive narrow bipolar event, negative narrow bipolar event, positive pre-breakdown process, negative pre-breakdown process, continuous multi-pulse cloud flash, bipolar pulse, skywave. A multi-layer one-dimensional convolutional neural network (1D-CNN) was designed to automatically extract VLF/LF lightning waveform features and distinguish lightning waveforms. The model achieved an overall accuracy of 99.11% in the lightning dataset and overall accuracy of 97.55% in a thunderstorm process. Considering its excellent performance, this model could be used in lightning sensors to assist in lightning monitoring and positioning.

Electrocardiogram (ECG) is a universal diagnostic tool for heart disease, which can provide data for deep learning. The scarcity of labeled data is a major challenge for medical artificial intelligence diagnosis. Acquiring labeled medical data is time-consuming and high-cost because medical specialists are needed. As a kind of generative self-supervised learning method, a masked autoencoder (MAE) is capable to solve these problems. Masked Autoencoder Family of Electrocardiogram (MaeFE) is proposed in this paper. Considering the temporal and spatial features of ECG, MaeFE contains three customized masking modes including Masked Time Autoencoder (MTAE), Masked Lead Autoencoder (MLAE), and Masked Lead and Time Autoencoder (MLTAE). MTAE and MLAE pay greater attention to temporal features and spatial features respectively. MLTAE is a multi-head architecture that combines MTAE and MLAE. In the pre-training stage, ECG signals from the pre-train dataset are divided into patches and partially masked. The encoder transfers unmasked patches to tokens and the decoder reconstructs masked ones. In downstream tasks, the pre-trained encoder is utilized as a classifier, which is arrhythmia classification performed in the downstream dataset. The process is the so-called transfer learning. MaeFE outperforms the state-of-the-art self-supervised learning methods, SimCLR, MoCo, CLOCS, and MaskUNet in downstream tasks. MTAE has the best comprehensive performance. Compared to contrastive learning models, MTAE achieves at least a 5.18%, 11.80%, and 3.23% increase in Acc, Macro-F₁ , and AUC respectively using the linear probe. It also outperforms other models at 8.99% in Acc , 20.18% in Macro-F₁ , and 7.13% in AUC respectively using fine-tuning. As another downstream task, experiments on the multi-label classification of arrhythmia are also conducted, which reflects the excellent generalization performance of MaeFE. Depending on experimental results, MaeFE turns out to be efficient and robust in downstream tasks. Overcoming the scarcity of labeled data, MaeFE is better than other self-supervised learning methods and achieves satisfying performance. Consequently, the algorithm in this paper is on track of playing a major role in practical applications.

Quantum machine learning is a promising application of quantum computing for data classification. However, most of the previous research focused on binary classification, and there are few studies on multi-classification. The major challenge comes from the limitations of near-term quantum devices on the number of qubits and the size of quantum circuits. In this paper, we propose a hybrid quantum neural network to implement multi-classification of a real-world dataset. We use an average pooling downsampling strategy to reduce the dimensionality of samples, and we design a ladder-like parameterized quantum circuit to disentangle the input states. Besides this, we adopt an all-qubit multi-observable measurement strategy to capture sufficient hidden information from the quantum system. The experimental results show that our algorithm outperforms the classical neural network and performs especially well on different multi-class datasets, which provides some enlightenment for the application of quantum computing to real-world data on near-term quantum processors.