A framework for cardiac arrhythmia detection from IoT-based ECGs
- He, Jinyuan, Rong, Jia, Sun, Le, Wang, Hua, Zhang, Yanchun, Ma, Jiangang
- Authors: He, Jinyuan , Rong, Jia , Sun, Le , Wang, Hua , Zhang, Yanchun , Ma, Jiangang
- Date: 2020
- Type: Text , Journal article
- Relation: World Wide Web Vol. 23, no. 5 (2020), p. 2835-2850
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- Description: Cardiac arrhythmia has been identified as a type of cardiovascular diseases (CVDs) that causes approximately 12% of all deaths globally. The development of Internet-of-Things has spawned novel ways for heart monitoring but also presented new challenges for manual arrhythmia detection. An automated method is highly demanded to provide support for physicians. Current attempts for automatic arrhythmia detection can roughly be divided as feature-engineering based and deep-learning based methods. Most of the feature-engineering based methods are suffering from adopting single classifier and use fixed features for classifying all five types of heartbeats. This introduces difficulties in identification of the problematic heartbeats and limits the overall classification performance. The deep-learning based methods are usually not evaluated in a realistic manner and report overoptimistic results which may hide potential limitations of the models. Moreover, the lack of consideration of frequency patterns and the heart rhythms can also limit the model performance. To fill in the gaps, we propose a framework for arrhythmia detection from IoT-based ECGs. The framework consists of two modules: a data cleaning module and a heartbeat classification module. Specifically, we propose two solutions for the heartbeat classification task, namely Dynamic Heartbeat Classification with Adjusted Features (DHCAF) and Multi-channel Heartbeat Convolution Neural Network (MCHCNN). DHCAF is a feature-engineering based approach, in which we introduce dynamic ensemble selection (DES) technique and develop a result regulator to improve classification performance. MCHCNN is deep-learning based solution that performs multi-channel convolutions to capture both temporal and frequency patterns from heartbeat to assist the classification. We evaluate the proposed framework with DHCAF and with MCHCNN on the well-known MIT-BIH-AR database, respectively. The results reported in this paper have proven the effectiveness of our framework. © 2020, Springer Science+Business Media, LLC, part of Springer Nature.
- Authors: He, Jinyuan , Rong, Jia , Sun, Le , Wang, Hua , Zhang, Yanchun , Ma, Jiangang
- Date: 2020
- Type: Text , Journal article
- Relation: World Wide Web Vol. 23, no. 5 (2020), p. 2835-2850
- Full Text:
- Reviewed:
- Description: Cardiac arrhythmia has been identified as a type of cardiovascular diseases (CVDs) that causes approximately 12% of all deaths globally. The development of Internet-of-Things has spawned novel ways for heart monitoring but also presented new challenges for manual arrhythmia detection. An automated method is highly demanded to provide support for physicians. Current attempts for automatic arrhythmia detection can roughly be divided as feature-engineering based and deep-learning based methods. Most of the feature-engineering based methods are suffering from adopting single classifier and use fixed features for classifying all five types of heartbeats. This introduces difficulties in identification of the problematic heartbeats and limits the overall classification performance. The deep-learning based methods are usually not evaluated in a realistic manner and report overoptimistic results which may hide potential limitations of the models. Moreover, the lack of consideration of frequency patterns and the heart rhythms can also limit the model performance. To fill in the gaps, we propose a framework for arrhythmia detection from IoT-based ECGs. The framework consists of two modules: a data cleaning module and a heartbeat classification module. Specifically, we propose two solutions for the heartbeat classification task, namely Dynamic Heartbeat Classification with Adjusted Features (DHCAF) and Multi-channel Heartbeat Convolution Neural Network (MCHCNN). DHCAF is a feature-engineering based approach, in which we introduce dynamic ensemble selection (DES) technique and develop a result regulator to improve classification performance. MCHCNN is deep-learning based solution that performs multi-channel convolutions to capture both temporal and frequency patterns from heartbeat to assist the classification. We evaluate the proposed framework with DHCAF and with MCHCNN on the well-known MIT-BIH-AR database, respectively. The results reported in this paper have proven the effectiveness of our framework. © 2020, Springer Science+Business Media, LLC, part of Springer Nature.
A new dimensionality-unbiased score for efficient and effective outlying aspect mining
- Samariya, Durgesh, Ma, Jiangang
- Authors: Samariya, Durgesh , Ma, Jiangang
- Date: 2022
- Type: Text , Journal article
- Relation: Data Science and Engineering Vol. 7, no. 2 (2022), p. 120-135
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- Description: The main aim of the outlying aspect mining algorithm is to automatically detect the subspace(s) (a.k.a. aspect(s)), where a given data point is dramatically different than the rest of the data in each of those subspace(s) (aspect(s)). To rank the subspaces for a given data point, a scoring measure is required to compute the outlying degree of the given data in each subspace. In this paper, we introduce a new measure to compute outlying degree, called Simple Isolation score using Nearest Neighbor Ensemble (SiNNE), which not only detects the outliers but also provides an explanation on why the selected point is an outlier. SiNNE is a dimensionally unbias measure in its raw form, which means the scores produced by SiNNE are compared directly with subspaces having different dimensions. Thus, it does not require any normalization to make the score unbiased. Our experimental results on synthetic and publicly available real-world datasets revealed that (i) SiNNE produces better or at least the same results as existing scores. (ii) It improves the run time of the existing outlying aspect mining algorithm based on beam search by at least two orders of magnitude. SiNNE allows the existing outlying aspect mining algorithm to run in datasets with hundreds of thousands of instances and thousands of dimensions which was not possible before. © 2022, The Author(s).
- Authors: Samariya, Durgesh , Ma, Jiangang
- Date: 2022
- Type: Text , Journal article
- Relation: Data Science and Engineering Vol. 7, no. 2 (2022), p. 120-135
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- Description: The main aim of the outlying aspect mining algorithm is to automatically detect the subspace(s) (a.k.a. aspect(s)), where a given data point is dramatically different than the rest of the data in each of those subspace(s) (aspect(s)). To rank the subspaces for a given data point, a scoring measure is required to compute the outlying degree of the given data in each subspace. In this paper, we introduce a new measure to compute outlying degree, called Simple Isolation score using Nearest Neighbor Ensemble (SiNNE), which not only detects the outliers but also provides an explanation on why the selected point is an outlier. SiNNE is a dimensionally unbias measure in its raw form, which means the scores produced by SiNNE are compared directly with subspaces having different dimensions. Thus, it does not require any normalization to make the score unbiased. Our experimental results on synthetic and publicly available real-world datasets revealed that (i) SiNNE produces better or at least the same results as existing scores. (ii) It improves the run time of the existing outlying aspect mining algorithm based on beam search by at least two orders of magnitude. SiNNE allows the existing outlying aspect mining algorithm to run in datasets with hundreds of thousands of instances and thousands of dimensions which was not possible before. © 2022, The Author(s).
Image preprocessing in classification and identification of diabetic eye diseases
- Sarki, Rubina, Ahmed, Khandakar, Wang, Hua, Zhang, Yanchun, Ma, Jiangang, Wang, Kate
- Authors: Sarki, Rubina , Ahmed, Khandakar , Wang, Hua , Zhang, Yanchun , Ma, Jiangang , Wang, Kate
- Date: 2021
- Type: Text , Journal article
- Relation: Data Science and Engineering Vol. 6, no. 4 (2021), p. 455-471
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- Description: Diabetic eye disease (DED) is a cluster of eye problem that affects diabetic patients. Identifying DED is a crucial activity in retinal fundus images because early diagnosis and treatment can eventually minimize the risk of visual impairment. The retinal fundus image plays a significant role in early DED classification and identification. An accurate diagnostic model’s development using a retinal fundus image depends highly on image quality and quantity. This paper presents a methodical study on the significance of image processing for DED classification. The proposed automated classification framework for DED was achieved in several steps: image quality enhancement, image segmentation (region of interest), image augmentation (geometric transformation), and classification. The optimal results were obtained using traditional image processing methods with a new build convolution neural network (CNN) architecture. The new built CNN combined with the traditional image processing approach presented the best performance with accuracy for DED classification problems. The results of the experiments conducted showed adequate accuracy, specificity, and sensitivity. © 2021, The Author(s).
- Authors: Sarki, Rubina , Ahmed, Khandakar , Wang, Hua , Zhang, Yanchun , Ma, Jiangang , Wang, Kate
- Date: 2021
- Type: Text , Journal article
- Relation: Data Science and Engineering Vol. 6, no. 4 (2021), p. 455-471
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- Description: Diabetic eye disease (DED) is a cluster of eye problem that affects diabetic patients. Identifying DED is a crucial activity in retinal fundus images because early diagnosis and treatment can eventually minimize the risk of visual impairment. The retinal fundus image plays a significant role in early DED classification and identification. An accurate diagnostic model’s development using a retinal fundus image depends highly on image quality and quantity. This paper presents a methodical study on the significance of image processing for DED classification. The proposed automated classification framework for DED was achieved in several steps: image quality enhancement, image segmentation (region of interest), image augmentation (geometric transformation), and classification. The optimal results were obtained using traditional image processing methods with a new build convolution neural network (CNN) architecture. The new built CNN combined with the traditional image processing approach presented the best performance with accuracy for DED classification problems. The results of the experiments conducted showed adequate accuracy, specificity, and sensitivity. © 2021, The Author(s).
PU-shapelets : Towards pattern-based positive unlabeled classification of time series
- Liang, Shen, Zhang, Yanchun, Ma, Jiangang
- Authors: Liang, Shen , Zhang, Yanchun , Ma, Jiangang
- Date: 2019
- Type: Text , Conference proceedings , Conference paper
- Relation: 24th International Conference on Database Systems for Advanced Applications, DASFAA 2019; Chiang Mai, Thailand; 22nd-25th April 2019; part of the Lecture Notes in Computer Science book series, also part of the Information Systems and Applications, incl. Internet/Web and HCI sub series Vol. 11446 LNCS, p. 87-103
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- Description: Real-world time series classification applications often involve positive unlabeled (PU) training data, where there are only a small set PL of positive labeled examples and a large set U of unlabeled ones. Most existing time series PU classification methods utilize all readings in the time series, making them sensitive to non-characteristic readings. Characteristic patterns named shapelets present a promising solution to this problem, yet discovering shapelets under PU settings is not easy. In this paper, we take on the challenging task of shapelet discovery with PU data. We propose a novel pattern ensemble technique utilizing both characteristic and non-characteristic patterns to rank U examples by their possibilities of being positive. We also present a novel stopping criterion to estimate the number of positive examples in U. These enable us to effectively label all U training examples and conduct supervised shapelet discovery. The shapelets are then used to build a one-nearest-neighbor classifier for online classification. Extensive experiments demonstrate the effectiveness of our method.
- Description: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
- Authors: Liang, Shen , Zhang, Yanchun , Ma, Jiangang
- Date: 2019
- Type: Text , Conference proceedings , Conference paper
- Relation: 24th International Conference on Database Systems for Advanced Applications, DASFAA 2019; Chiang Mai, Thailand; 22nd-25th April 2019; part of the Lecture Notes in Computer Science book series, also part of the Information Systems and Applications, incl. Internet/Web and HCI sub series Vol. 11446 LNCS, p. 87-103
- Full Text:
- Reviewed:
- Description: Real-world time series classification applications often involve positive unlabeled (PU) training data, where there are only a small set PL of positive labeled examples and a large set U of unlabeled ones. Most existing time series PU classification methods utilize all readings in the time series, making them sensitive to non-characteristic readings. Characteristic patterns named shapelets present a promising solution to this problem, yet discovering shapelets under PU settings is not easy. In this paper, we take on the challenging task of shapelet discovery with PU data. We propose a novel pattern ensemble technique utilizing both characteristic and non-characteristic patterns to rank U examples by their possibilities of being positive. We also present a novel stopping criterion to estimate the number of positive examples in U. These enable us to effectively label all U training examples and conduct supervised shapelet discovery. The shapelets are then used to build a one-nearest-neighbor classifier for online classification. Extensive experiments demonstrate the effectiveness of our method.
- Description: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
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