Physiological Sensor Signals Classification for Healthcare Using Sensor Data Fusion and Case-Based Reasoning

Begum, Shahina; Barua, Shaibal; Ahmed, Mobyen Uddin

doi:10.3390/s140711770

Cited by 48 publications

(23 citation statements)

References 25 publications

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“…Some works in sensing-based healthcare are focused on reducing big data collection using aggregation, compression, and prediction methods [6][7][8][9][10][11][12]. The authors of [6] proposed the Priority-based Compressed Data Aggregation (PCDA) technique to reduce the amount of heath data transmitted.…”

Section: Related Workmentioning

confidence: 99%

A Hadoop-Based Platform for Patient Classification and Disease Diagnosis in Healthcare Applications

Harb

Mroue

Mansour

et al. 2020

Sensors

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Nowadays, the increasing number of patients accompanied with the emergence of new symptoms and diseases makes heath monitoring and assessment a complicated task for medical staff and hospitals. Indeed, the processing of big and heterogeneous data collected by biomedical sensors along with the need of patients' classification and disease diagnosis become major challenges for several health-based sensing applications. Thus, the combination between remote sensing devices and the big data technologies have been proven as an efficient and low cost solution for healthcare applications. In this paper, we propose a robust big data analytics platform for real time patient monitoring and decision making to help both hospital and medical staff. The proposed platform relies on big data technologies and data analysis techniques and consists of four layers: real time patient monitoring, real time decision and data storage, patient classification and disease diagnosis, and data retrieval and visualization. To evaluate the performance of our platform, we implemented our platform based on the Hadoop ecosystem and we applied the proposed algorithms over real health data. The obtained results show the effectiveness of our platform in terms of efficiently performing patient classification and disease diagnosis in healthcare applications.

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Section: Related Workmentioning

confidence: 99%

A Hadoop-Based Platform for Patient Classification and Disease Diagnosis in Healthcare Applications

Harb

Mroue

Mansour

et al. 2020

Sensors

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“…Another example of data fusion in healthcare is proposed in article by Yang and Huang [36], where Kinect and color cameras are combined together to perform human tracking and identification. Begum et al [37] make an attempt to classify "stressed" and "relaxed" individuals fusing data from various physiological sensors, that is, Heart Rate, Finger Temperature, Respiration Rate, Carbon Dioxide, and Oxygen Saturation. In this case fusion algorithm performed on decision and data level is additionally combined with Case-Based Reasoning for further classification.…”

Section: Sensor Fusion In Fall Detectionmentioning

confidence: 99%

“…In this case, continuous data collected from multiple sources can be analyzed for reoccurring patterns as suggested in our previous publication [38]. Multisensor fusion has proved its efficiency in various areas of the healthcare domain [37] and subsequently gained its popularity in fall detection domain. Moreover, with a recent development on ICT market more sensors are now available and can be combined to perform advanced level of activity tracking, which will increase number of publications.…”

Section: Sensor Fusion In Fall Detectionmentioning

confidence: 99%

Challenges and Issues in Multisensor Fusion Approach for Fall Detection: Review Paper

2016

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Emergency situations associated with falls are a serious concern for an aging society. Yet following the recent development within ICT, a significant number of solutions have been proposed to track body movement and detect falls using various sensor technologies, thereby facilitating fall detection and in some cases prevention. A number of recent reviews on fall detection methods using ICT technologies have emerged in the literature and an increasingly popular approach considers combining information from several sensor sources to assess falls. The aim of this paper is to review in detail the subfield of fall detection techniques that explicitly considers the use of multisensor fusion based methods to assess and determine falls. The paper highlights key differences between the single sensor-based approach and a multifusion one. The paper also describes and categorizes the various systems used, provides information on the challenges of a multifusion approach, and finally discusses trends for future work.

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“…In this paper, CBR [61][62][63][64][65] and Fuzzification [40,66,67] has been applied to facilitate decision-making. Retrieval of similar past cases to solve the current problem is the first step of the system.…”

Section: Decision Supportmentioning

confidence: 99%

In-Vehicle Stress Monitoring Based on EEG Signal

Begum¹,

Ahmed²

2017

IJERA

Self Cite

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In recent years, improved road safety by monitoring human factors i.e., stress, mental load, sleepiness, fatigue etc. of vehicle drivers has been addressed in a number of studies. Due to the individual variations and complex dynamic in-vehicle environment systems that can monitor such factors of a driver while driving is challenging. This paper presents a drivers' stress monitoring system based on electroencephalography (EEG) signals enabling individual---focused computational approach that can generate automatic decision. Here, a combination of different signal processing i.e., discrete wavelet transform, largest Lyapunov exponent (LLE) and modified covariance have been applied to extract key features from the EEG signals. Hybrid classification approach Fuzzy-CBR (case-based reasoning) is used for decision support. The study has focused on both long and short-term temporal assessment of EEG signals enabling monitoring in different time intervals. In short time interval, which requires complex computations, the classification accuracy using the proposed approach is 79% compare to a human expert. Accuracy of EEG in developing such system is also compared with other reference signals e.g., Electrocardiography (ECG), Finger temperature, Skin conductance, and Respiration. The results show that in decision making the system can handle individual variations and provides decision in each minute time interval.

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Physiological Sensor Signals Classification for Healthcare Using Sensor Data Fusion and Case-Based Reasoning

Cited by 48 publications

References 25 publications

A Hadoop-Based Platform for Patient Classification and Disease Diagnosis in Healthcare Applications

A Hadoop-Based Platform for Patient Classification and Disease Diagnosis in Healthcare Applications

Challenges and Issues in Multisensor Fusion Approach for Fall Detection: Review Paper

In-Vehicle Stress Monitoring Based on EEG Signal

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