Automatic Classification of Fetal Heart Rate Based on Convolutional Neural Network

Li, Jianqiang; Chen, Zhuangzhuang; Huang, Luxiang; Fang, Min; Li, Bing; Fu, Xianghua; Wang, Huihui; Zhao, Qingguo

doi:10.1109/jiot.2018.2845128

Cited by 67 publications

(37 citation statements)

References 31 publications

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“…Another work has suggested a Multiple Kernel Learning with Adaptive Neuro-Fuzzy Inference System (MKL with ANFIS) for heart disease diagnosis and has produced high sensitivity (98%) and high specificity (99%) for the KEGG Metabolic Reaction Network dataset [47]. Recent applications implemented a convolutional neural network (CNN) and multilayer perceptron (MLP) for fetal heart rate records assessment and reached 85% accuracy [48]; a recurrent neural network (RNN) was also suggested for automatic detection of irregular beating rhythm in records with 83% accuracy [49]. A long-short term memory (LSTM) network was used for atrial fibrillation classification from diverse electrocardiographic signals and reached 78% accuracy in [50], and 79% F1 score in [51].…”

Section: Related Workmentioning

confidence: 99%

Ensemble Deep Learning Models for Heart Disease Classification: A Case Study from Mexico

et al. 2020

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Heart diseases are highly ranked among the leading causes of mortality in the world. They have various types including vascular, ischemic, and hypertensive heart disease. A large number of medical features are reported for patients in the Electronic Health Records (EHR) that allow physicians to diagnose and monitor heart disease. We collected a dataset from Medica Norte Hospital in Mexico that includes 800 records and 141 indicators such as age, weight, glucose, blood pressure rate, and clinical symptoms. Distribution of the collected records is very unbalanced on the different types of heart disease, where 17% of records have hypertensive heart disease, 16% of records have ischemic heart disease, 7% of records have mixed heart disease, and 8% of records have valvular heart disease. Herein, we propose an ensemble-learning framework of different neural network models, and a method of aggregating random under-sampling. To improve the performance of the classification algorithms, we implement a data preprocessing step with features selection. Experiments were conducted with unidirectional and bidirectional neural network models and results showed that an ensemble classifier with a BiLSTM or BiGRU model with a CNN model had the best classification performance with accuracy and F1-score between 91% and 96% for the different types of heart disease. These results are competitive and promising for heart disease dataset. We showed that ensemble-learning framework based on deep models could overcome the problem of classifying an unbalanced heart disease dataset. Our proposed framework can lead to highly accurate models that are adapted for clinical real data and diagnosis use.

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Section: Related Workmentioning

confidence: 99%

Ensemble Deep Learning Models for Heart Disease Classification: A Case Study from Mexico

et al. 2020

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“…The batch gradient descent algorithm was applied to facilitate the CNN converge with the global optimum. Finally, the FC layer was connected to the softmax function (loss, shown in Table 1) to obtain the last output [22].…”

Section: Methodsmentioning

confidence: 99%

“…Different results were produced because of different hyperparameter values at each training of the CNN [22]. The repetition of each experiment process is called an “iteration” [33].…”

Section: Methodsmentioning

confidence: 99%

“…The repetition of each experiment process is called an “iteration” [33]. The standard deviation was set to 0.1, and some small positive values (0, 1) were added to the bias to avoid dead nodes [22]. The dropout (dropout_ratio) was set to 0.5 to gain the best results.…”

Section: Methodsmentioning

confidence: 99%

“…The convolutional neural network (CNN) has recently been applied to obstetrics and gynecology [21] for classification of the fetal heart rate [22], electromyography [23], and electrocardiogram signals [24]. The CNN is a type of machine learning which can classify images and time series without additional feature extraction and selection and produce state-of-the-art recognition results.…”

Section: Introductionmentioning

confidence: 99%

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Preliminary Study on the Efficient Electrohysterogram Segments for Recognizing Uterine Contractions with Convolutional Neural Networks

Jin

Hao

Liu

et al. 2019

BioMed Research International

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Background Uterine contraction (UC) is the tightening and shortening of the uterine muscles which can indicate the progress of pregnancy towards delivery. Electrohysterogram (EHG), which reflects uterine electrical activities, has recently been studied for UC monitoring. In this paper, we aimed to evaluate different EHG segments for recognizing UCs using the convolutional neural network (CNN). Materials and Methods In the open-access Icelandic 16-electrode EHG database (122 recordings from 45 pregnant women), 7136 UC and 7136 non-UC EHG segments with the duration of 60 s were manually extracted from 107 recordings of 40 pregnant women to develop a CNN model. A fivefold cross-validation was applied to evaluate the CNN based on sensitivity (SE), specificity (SP), and accuracy (ACC). Then, 1056 UC and 1056 non-UC EHG segments were extracted from the other 15 recordings of 5 pregnant women. Furthermore, the developed CNN model was applied to identify UCs using different EHG segments with the durations of 10 s, 20 s, and 30 s. Results The CNN achieved the average SE, SP, and ACC of 0.82, 0.93, and 0.88 for a 60 s EHG segment. The EHG segments of 10 s, 20 s, and 30 s around the TOCO peak achieved higher SE and ACC than the other segments with the same duration. The values of SE from 20 s EHG segments around the TOCO peak were higher than those from 10 s to 30 s EHG segments on the same side of the TOCO peak. Conclusion The proposed method could be used to determine the efficient EHG segments for recognizing UC with the CNN.

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Predicting intensive care unit bed occupancy for integrated operating room scheduling via neural networks

Schiele

Koperna

Brunner

2020

Naval Research Logistics

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In a master surgery scheduling (MSS) problem, a hospital's operating room (OR) capacity is assigned to different medical specialties. This task is critical since the risk of assigning too much or too little OR time to a specialty is associated with overtime or deficit hours of the staff, deferral or delay of surgeries, and unsatisfied—or even endangered—patients. Most MSS approaches in the literature focus only on the OR while neglecting the impact on downstream units or reflect a simplified version of the real‐world situation. We present the first prediction model for the integrated OR scheduling problem based on machine learning. Our three‐step approach focuses on the intensive care unit (ICU) and reflects elective and urgent patients, inpatients and outpatients, and all possible paths through the hospital. We provide an empirical evaluation of our method with surgery data for Universitätsklinikum Augsburg, a German tertiary care hospital with 1700 beds. We show that our model outperforms a state‐of‐the‐art model by 43% in number of predicted beds. Our model can be used as supporting tool for hospital managers or incorporated in an optimization model. Eventually, we provide guidance to support hospital managers in scheduling surgeries more efficiently.

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Automatic Classification of Fetal Heart Rate Based on Convolutional Neural Network

Cited by 67 publications

References 31 publications

Ensemble Deep Learning Models for Heart Disease Classification: A Case Study from Mexico

Ensemble Deep Learning Models for Heart Disease Classification: A Case Study from Mexico

Preliminary Study on the Efficient Electrohysterogram Segments for Recognizing Uterine Contractions with Convolutional Neural Networks

Predicting intensive care unit bed occupancy for integrated operating room scheduling via neural networks

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