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General General

Variable step dynamic threshold local binary pattern for classification of atrial fibrillation.

In Artificial intelligence in medicine ; h5-index 34.0

OBJECTIVE : In this paper, we proposed new methods for feature extraction in machine learning-based classification of atrial fibrillation from ECG signal.

METHODS : Our proposed methods improved conventional 1-dimensional local binary pattern method in two ways. First, we proposed a dynamic threshold LBP code generation method for use with 1-dimensional signals, enabling the generated LBP codes to have a more detailed representation of the signal morphological pattern. Second, we introduced a variable step value into the LBP code generation algorithm to better cope with a high sampling frequency input signal without a downsampling process. The proposed methods do not employ computationally expensive processes such as filtering, wavelet transform, up/downsampling, or beat detection, and can be implemented using only simple addition, division, and compare operations.

RESULTS : Combining these two approaches, our proposed variable step dynamic threshold local binary pattern method achieved 99.11% sensitivity and 99.29% specificity when used as a feature generation algorithm in support vector machine classification of atrial fibrillation from MIT-BIH Atrial Fibrillation Database dataset. When applied on signals from MIT-BIH Arrhythmia Database, our proposed method achieved similarly good 99.38% sensitivity and 98.97% specificity.

CONCLUSION : Our proposed methods achieved one of the best results among published works in atrial fibrillation classification using the same dataset while using less computationally expensive calculations, without significant performance degradation when applied on signals from multiple databases with different sampling frequencies.

Yazid Muhammad, Abdur Rahman Mahrus

2020-Aug

AFDB, Atrial fibrillation, Dynamic threshold, Local binary pattern, MITDB, Variable step

General General

Indoor location identification of patients for directing virtual care: An AI approach using machine learning and knowledge-based methods.

In Artificial intelligence in medicine ; h5-index 34.0

In a digitally enabled healthcare setting, we posit that an individual's current location is pivotal for supporting many virtual care services-such as tailoring educational content towards an individual's current location, and, hence, current stage in an acute care process; improving activity recognition for supporting self-management in a home-based setting; and guiding individuals with cognitive decline through daily activities in their home. However, unobtrusively estimating an individual's indoor location in real-world care settings is still a challenging problem. Moreover, the needs of location-specific care interventions go beyond absolute coordinates and require the individual's discrete semantic location; i.e., it is the concrete type of an individual's location (e.g., exam vs. waiting room; bathroom vs. kitchen) that will drive the tailoring of educational content or recognition of activities. We utilized Machine Learning methods to accurately identify an individual's discrete location, together with knowledge-based models and tools to supply the associated semantics of identified locations. We considered clustering solutions to improve localization accuracy at the expense of granularity; and investigate sensor fusion-based heuristics to rule out false location estimates. We present an AI-driven indoor localization approach that integrates both data-driven and knowledge-based processes and artifacts. We illustrate the application of our approach in two compelling healthcare use cases, and empirically validated our localization approach at the emergency unit of a large Canadian pediatric hospital.

Van Woensel William, Roy Patrice C, Abidi Syed Sibte Raza, Abidi Samina Raza

2020-Aug

Activities of daily living, Ambient assisted living, Ambient sensors, Data fusion, Indoor localization, Machine learning, Self-management, Semantic web, Virtual care, eHealth platform

Pathology Pathology

Using topological data analysis and pseudo time series to infer temporal phenotypes from electronic health records.

In Artificial intelligence in medicine ; h5-index 34.0

Temporal phenotyping enables clinicians to better understand observable characteristics of a disease as it progresses. Modelling disease progression that captures interactions between phenotypes is inherently challenging. Temporal models that capture change in disease over time can identify the key features that characterize disease subtypes that underpin these trajectories. These models will enable clinicians to identify early warning signs of progression in specific sub-types and therefore to make informed decisions tailored to individual patients. In this paper, we explore two approaches to building temporal phenotypes based on the topology of data: topological data analysis and pseudo time-series. Using type 2 diabetes data, we show that the topological data analysis approach is able to identify disease trajectories and that pseudo time-series can infer a state space model characterized by transitions between hidden states that represent distinct temporal phenotypes. Both approaches highlight lipid profiles as key factors in distinguishing the phenotypes.

Dagliati Arianna, Geifman Nophar, Peek Niels, Holmes John H, Sacchi Lucia, Bellazzi Riccardo, Sajjadi Seyed Erfan, Tucker Allan

2020-Aug

Electronic phenotyping, Longitudinal studies, Type 2 diabetes, Unsupervised machine learning

Surgery Surgery

Deep learning to find colorectal polyps in colonoscopy: A systematic literature review.

In Artificial intelligence in medicine ; h5-index 34.0

Colorectal cancer has a great incidence rate worldwide, but its early detection significantly increases the survival rate. Colonoscopy is the gold standard procedure for diagnosis and removal of colorectal lesions with potential to evolve into cancer and computer-aided detection systems can help gastroenterologists to increase the adenoma detection rate, one of the main indicators for colonoscopy quality and predictor for colorectal cancer prevention. The recent success of deep learning approaches in computer vision has also reached this field and has boosted the number of proposed methods for polyp detection, localization and segmentation. Through a systematic search, 35 works have been retrieved. The current systematic review provides an analysis of these methods, stating advantages and disadvantages for the different categories used; comments seven publicly available datasets of colonoscopy images; analyses the metrics used for reporting and identifies future challenges and recommendations. Convolutional neural networks are the most used architecture together with an important presence of data augmentation strategies, mainly based on image transformations and the use of patches. End-to-end methods are preferred over hybrid methods, with a rising tendency. As for detection and localization tasks, the most used metric for reporting is the recall, while Intersection over Union is highly used in segmentation. One of the major concerns is the difficulty for a fair comparison and reproducibility of methods. Even despite the organization of challenges, there is still a need for a common validation framework based on a large, annotated and publicly available database, which also includes the most convenient metrics to report results. Finally, it is also important to highlight that efforts should be focused in the future on proving the clinical value of the deep learning based methods, by increasing the adenoma detection rate.

Sánchez-Peralta Luisa F, Bote-Curiel Luis, Picón Artzai, Sánchez-Margallo Francisco M, Pagador J Blas

2020-Aug

Colorectal cancer, Deep learning, Detection, Localization, Segmentation

General General

Continuous blood pressure measurement from one-channel electrocardiogram signal using deep-learning techniques.

In Artificial intelligence in medicine ; h5-index 34.0

Continuous blood pressure (BP) measurement is crucial for reliable and timely hypertension detection. State-of-the-art continuous BP measurement methods based on pulse transit time or multiple parameters require simultaneous electrocardiogram (ECG) and photoplethysmogram (PPG) signals. Compared with PPG signals, ECG signals are easy to collect using wearable devices. This study examined a novel continuous BP estimation approach using one-channel ECG signals for unobtrusive BP monitoring. A BP model is developed based on the fusion of a residual network and long short-term memory to obtain the spatial-temporal information of ECG signals. The public multiparameter intelligent monitoring waveform database, which contains ECG, PPG, and invasive BP data of patients in intensive care units, is used to develop and verify the model. Experimental results demonstrated that the proposed approach exhibited an estimation error of 0.07 ± 7.77 mmHg for mean arterial pressure (MAP) and 0.01 ± 6.29 for diastolic BP (DBP), which comply with the Association for the Advancement of Medical Instrumentation standard. According to the British Hypertension Society standards, the results achieved grade A for MAP and DBP estimation and grade B for systolic BP (SBP) estimation. Furthermore, we verified the model with an independent dataset for arrhythmia patients. The experimental results exhibited an estimation error of -0.22 ± 5.82 mmHg, -0.57 ± 4.39 mmHg, and -0.75 ± 5.62 mmHg for SBP, MAP, and DBP measurements, respectively. These results indicate the feasibility of estimating BP by using a one-channel ECG signal, thus enabling continuous BP measurement for ubiquitous health care applications.

Miao Fen, Wen Bo, Hu Zhejing, Fortino Giancarlo, Wang Xi-Ping, Liu Zeng-Ding, Tang Min, Li Ye

2020-Aug

Blood pressure, ECG, Long short-term memory, Residual network

General General

Enhancing Quality of Patients Care and Improving Patient Experience in China with Assistance of Artificial Intelligence.

In Chinese medical sciences journal = Chung-kuo i hsueh k'o hsueh tsa chih

Improving health of Chinese people has become national strategy according to the Healthy China 2030. Patient experience evaluation examines health care service from perspective of patients; it is important for improving health care quality. Applying artificial intelligence (AI) in patient experience is an innovative approach to assist continuous improvement of care quality of patient service. A nursing quality platform based on patient experience data which is empowered by AI technologies has been established in China for the purpose of surveillance and analysis of the quality of patient care. It contains data from nearly 1300 healthcare facilities, based on which portraits of nursing service qualities can be drawn. The patient experience big data platform has shown potentials for healthcare facilities to improve patient care quality. More efforts are needed to achieve the goal of enhancing people's sense of health gain.

Wang Zheng, Zhao Qing Hua, Yang Jing Lin, Zhou Feng

2020-Sep-30