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Article ; Online: A Novel Medical Device for Early Detection of Melanoma.

Afifi, Shereen / Gholamhosseini, Hamid / Sinha, Roopak / Lindén, Maria

Studies in health technology and informatics

2019 Volume 261, Page(s) 122–127

Abstract: Melanoma is the deadliest form of skin cancer. Early detection of melanoma is vital, as it helps in decreasing the death rate as well as treatment costs. Dermatologists are using image-based diagnostic tools to assist them in decision-making and ... ...

Abstract	Melanoma is the deadliest form of skin cancer. Early detection of melanoma is vital, as it helps in decreasing the death rate as well as treatment costs. Dermatologists are using image-based diagnostic tools to assist them in decision-making and detecting melanoma at an early stage. We aim to develop a novel handheld medical scanning device dedicated to early detection of melanoma at the primary healthcare with low cost and high performance. However, developing this particular device is very challenging due to the complicated computations required by the embedded diagnosis system. In this paper, we propose a hardware-friendly design for implementing an embedded system by exploiting the recent hardware advances in reconfigurable computing. The developed embedded system achieved optimized implementation results for the hardware resource utilization, power consumption, detection speed and processing time with high classification accuracy rate using real data for melanoma detection. Consequently, the proposed embedded diagnosis system meets the critical embedded systems constraints, which is capable for integration towards a cost- and energy-efficient medical device for early detection of melanoma.
MeSH term(s)	Computer-Aided Design ; Computers ; Early Detection of Cancer ; Humans ; Melanoma/diagnosis ; Skin Neoplasms/diagnosis
Language	English
Publishing date	2019-06-26
Publishing country	Netherlands
Document type	Journal Article
ISSN	1879-8365
ISSN (online)	1879-8365
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: Cuffless Blood Pressure Estimation Using Pulse Transit Time and Photoplethysmogram Intensity Ratio.

Gholamhosseini, Hamid / Baig, Mirza / Rastegar, Solmaz / Lindén, Maria

Studies in health technology and informatics

2018 Volume 249, Page(s) 77–83

Abstract: High blood pressure (BP) is one of the common risk factors for heart disease, stroke, congestive heart failure, and kidney disease. An accurate, continuous and cuffless BP monitoring technique could help clinicians improve the rate of prevention, ... ...

Abstract	High blood pressure (BP) is one of the common risk factors for heart disease, stroke, congestive heart failure, and kidney disease. An accurate, continuous and cuffless BP monitoring technique could help clinicians improve the rate of prevention, detection, and treatment of hypertension and related diseases. Pulse transit time (PTT) has attracted interest as an index of BP changes for cuffless BP measurement techniques. Currently, PPT-based BP measurement approaches have improved and are able to relieve the discomfort associated with an inflated cuff such as that used in auscultatory and oscillometric BP measurement techniques. However, PTT can only track the BP variation in high frequency (HF) which limits the true representation of BP changes. This paper presents a continuous and cuffless BP monitoring method based on multi-parameter fusion. We used photoplethysmogram (PPG) and a two-lead electrocardiogram (ECG) and employed an algorithm based on PTT and the PPG intensity ratio (PIR) to continuously track BP in both high and low frequencies and estimate systolic and diastolic BP.
MeSH term(s)	Blood Pressure ; Blood Pressure Determination/instrumentation ; Electrocardiography ; Humans ; Hypertension/diagnosis ; Photoplethysmography ; Pulse Wave Analysis
Language	English
Publishing date	2018-06-01
Publishing country	Netherlands
Document type	Journal Article
ISSN	0926-9630
ISSN	0926-9630
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Article ; Online: Estimating Systolic Blood Pressure Using Convolutional Neural Networks.

Rastegar, Solmaz / Gholamhosseini, Hamid / Lowe, Andrew / Mehdipour, Farhad / Lindén, Maria

Studies in health technology and informatics

2019 Volume 261, Page(s) 143–149

Abstract: Continuous blood pressure (BP) monitoring can produce a significant amount of digital data, which increases the chance of early diagnosis and improve the rate of survival for people diagnosed with hypertension and Cardiovascular diseases (CVDs). However, ...

Abstract	Continuous blood pressure (BP) monitoring can produce a significant amount of digital data, which increases the chance of early diagnosis and improve the rate of survival for people diagnosed with hypertension and Cardiovascular diseases (CVDs). However, mining and processing this vast amount of data are challenging. This research is aimed to address this challenge by proposing a deep learning technique, convolutional neural network (CNN), to estimate the systolic blood pressure (SBP) using electrocardiogram (ECG) and photoplethysmography (PPG) signals. Two different methods are investigated and compared in this research. In the first method, continuous wavelet transform (CWT) and CNN have been employed to estimate the SBP. For the second method, we used random sampling within the stochastic gradient descent (SGD) optimization of CNN and the raw ECG and PPG signals for training the network. The Medical Information Mart for Intensive Care (MIMIC III) database is used for both methods, which split to two parts, 70% for training our network and the remaining used for testing the performance of the network. Both methods are capable of learning how to extract relevant features from the signals. Therefore, there is no need for engineered feature extraction compared to previous works. Our experimental results show high accuracy for both CNN-based methods which make them promising and reliable architectures for SBP estimation.
MeSH term(s)	Blood Pressure ; Blood Pressure Determination ; Electrocardiography ; Humans ; Neural Networks (Computer) ; Photoplethysmography
Language	English
Publishing date	2019-06-26
Publishing country	Netherlands
Document type	Journal Article
ISSN	1879-8365
ISSN (online)	1879-8365
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Obesity Risk Assessment Model Using Wearable Technology with Personalized Activity, Calorie Expenditure and Health Profile.

Gholamhosseini, Hamid / Baig, Mirza / Maratas, Joseph / Mirza, Farhaan / Lindén, Maria

Studies in health technology and informatics

2019 Volume 261, Page(s) 91–96

Abstract: There is a worldwide increase in the rate of obesity and its related long-term conditions, emphasizing an immediate need to address this modern-age global epidemic of healthy living. Moreover, healthcare spending on long-term or chronic care conditions ... ...

Abstract	There is a worldwide increase in the rate of obesity and its related long-term conditions, emphasizing an immediate need to address this modern-age global epidemic of healthy living. Moreover, healthcare spending on long-term or chronic care conditions such as obesity is increasing to the point that requires effective interventions and advancements to reduce the burden of the healthcare. This research focuses on the early risk assessment of overweight/obesity using wearable technology. We establish an individualised health profile that identifies the level of activity and current health status of an individual using real-time activity and vital signs. We developed an algorithm to assess the risk of obesity using the individual's current activity and calorie expenditure. The algorithm was deployed on a smartphone application to collect wearable device data, and user reported data. Based on the collected data, the proposed application assesses the risk of obesity/overweight, measures the current activity level and recommends an optimized calorie plan.
MeSH term(s)	Energy Metabolism ; Humans ; Obesity ; Overweight ; Risk Assessment ; Wearable Electronic Devices
Language	English
Publishing date	2019-06-26
Publishing country	Netherlands
Document type	Journal Article
ISSN	1879-8365
ISSN (online)	1879-8365
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: Medical Device Integrated Vital Signs Monitoring Application with Real-Time Clinical Decision Support.

Moqeem, Aasia / Baig, Mirza / Gholamhosseini, Hamid / Mirza, Farhaan / Lindén, Maria

Studies in health technology and informatics

2018 Volume 249, Page(s) 189–193

Abstract: This research involves the design and development of a novel Android smartphone application for real-time vital signs monitoring and decision support. The proposed application integrates market available, wireless and Bluetooth connected medical devices ... ...

Abstract	This research involves the design and development of a novel Android smartphone application for real-time vital signs monitoring and decision support. The proposed application integrates market available, wireless and Bluetooth connected medical devices for collecting vital signs. The medical device data collected by the app includes heart rate, oxygen saturation and electrocardiograph (ECG). The collated data is streamed/displayed on the smartphone in real-time. This application was designed by adopting six screens approach (6S) mobile development framework and focused on user-centered approach and considered clinicians-as-a-user. The clinical engagement, consultations, feedback and usability of the application in the everyday practices were considered critical from the initial phase of the design and development. Furthermore, the proposed application is capable to deliver rich clinical decision support in real-time using the integrated medical device data.
MeSH term(s)	Decision Support Systems, Clinical ; Electrocardiography ; Feedback ; Heart Rate ; Humans ; Mobile Applications ; Oximetry ; Smartphone ; Vital Signs
Language	English
Publishing date	2018-06-01
Publishing country	Netherlands
Document type	Journal Article
ISSN	0926-9630
ISSN	0926-9630
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Smart health monitoring systems: an overview of design and modeling.

Baig, Mirza Mansoor / Gholamhosseini, Hamid

Journal of medical systems

2013 Volume 37, Issue 2, Page(s) 9898

Abstract: Health monitoring systems have rapidly evolved during the past two decades and have the potential to change the way health care is currently delivered. Although smart health monitoring systems automate patient monitoring tasks and, thereby improve the ... ...

Abstract	Health monitoring systems have rapidly evolved during the past two decades and have the potential to change the way health care is currently delivered. Although smart health monitoring systems automate patient monitoring tasks and, thereby improve the patient workflow management, their efficiency in clinical settings is still debatable. This paper presents a review of smart health monitoring systems and an overview of their design and modeling. Furthermore, a critical analysis of the efficiency, clinical acceptability, strategies and recommendations on improving current health monitoring systems will be presented. The main aim is to review current state of the art monitoring systems and to perform extensive and an in-depth analysis of the findings in the area of smart health monitoring systems. In order to achieve this, over fifty different monitoring systems have been selected, categorized, classified and compared. Finally, major advances in the system design level have been discussed, current issues facing health care providers, as well as the potential challenges to health monitoring field will be identified and compared to other similar systems.
MeSH term(s)	Confidentiality ; Humans ; Monitoring, Ambulatory/methods ; Remote Sensing Technology ; Telemedicine/instrumentation ; Telemedicine/methods ; Wireless Technology/organization & administration
Language	English
Publishing date	2013-01-15
Publishing country	United States
Document type	Journal Article ; Review
ZDB-ID	423488-1
ISSN	1573-689X ; 0148-5598
ISSN (online)	1573-689X
ISSN	0148-5598
DOI	10.1007/s10916-012-9898-z
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Zs.A 1426: Show issues

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Article: Smartphone-based Continuous Blood Pressure Measurement Using Pulse Transit Time.

Gholamhosseini, Hamid / Meintjes, Andries / Baig, Mirza / Linden, Maria

Studies in health technology and informatics

2016 Volume 224, Page(s) 84–89

Abstract: The increasing availability of low cost and easy to use personalized medical monitoring devices has opened the door for new and innovative methods of health monitoring to emerge. Cuff-less and continuous methods of measuring blood pressure are ... ...

Abstract	The increasing availability of low cost and easy to use personalized medical monitoring devices has opened the door for new and innovative methods of health monitoring to emerge. Cuff-less and continuous methods of measuring blood pressure are particularly attractive as blood pressure is one of the most important measurements of long term cardiovascular health. Current methods of noninvasive blood pressure measurement are based on inflation and deflation of a cuff with some effects on arteries where blood pressure is being measured. This inflation can also cause patient discomfort and alter the measurement results. In this work, a mobile application was developed to collate the PhotoPlethysmoGramm (PPG) waveform provided by a pulse oximeter and the electrocardiogram (ECG) for calculating the pulse transit time. This information is then indirectly related to the user's systolic blood pressure. The developed application successfully connects to the PPG and ECG monitoring devices using Bluetooth wireless connection and stores the data onto an online server. The pulse transit time is estimated in real time and the user's systolic blood pressure can be estimated after the system has been calibrated. The synchronization between the two devices was found to pose a challenge to this method of continuous blood pressure monitoring. However, the implemented continuous blood pressure monitoring system effectively serves as a proof of concept. This combined with the massive benefits that an accurate and robust continuous blood pressure monitoring system would provide indicates that it is certainly worthwhile to further develop this system.
Language	English
Publishing date	2016
Publishing country	Netherlands
Document type	Journal Article
ISSN	0926-9630
ISSN	0926-9630
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Falls risk assessment for hospitalised older adults: a combination of motion data and vital signs.

Baig, Mirza Mansoor / Gholamhosseini, Hamid / Connolly, Martin J

Aging clinical and experimental research

2016 Volume 28, Issue 6, Page(s) 1159–1168

Abstract: Health monitoring systems have rapidly evolved during the past two decades and have the potential to change the way healthcare is currently delivered. Currently hospital falls are a major healthcare concern worldwide because of the ageing population. ... ...

Abstract	Health monitoring systems have rapidly evolved during the past two decades and have the potential to change the way healthcare is currently delivered. Currently hospital falls are a major healthcare concern worldwide because of the ageing population. Current observational data and vital signs give the critical information related to the patient's physiology, and motion data provide an additional tool in falls risk assessment. These data combined with the patient's medical history potentially may give the interpretation model high information accessibility to predict falls risk. This study aims to develop a robust falls risk assessment system, in order to avoid falls and its related long-term disabilities in hospitals especially among older adults. The proposed system employs real-time vital signs, motion data, falls history and other clinical information. The falls risk assessment model has been tested and evaluated with 30 patients. The results of the proposed system have been compared with and evaluated against the hospital's falls scoring scale.
MeSH term(s)	Accidental Falls ; Aged ; Aging/physiology ; Hospitalization ; Humans ; Middle Aged ; Models, Theoretical ; Physical Examination ; Risk Assessment/methods
Language	English
Publishing date	2016-12
Publishing country	Germany
Document type	Journal Article
ZDB-ID	2104785-6
ISSN	1720-8319 ; 1594-0667
ISSN (online)	1720-8319
ISSN	1594-0667
DOI	10.1007/s40520-015-0510-5
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Zs.A 3343: Show issues

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Article ; Online: A novel approach for removing ECG interferences from surface EMG signals using a combined ANFIS and wavelet.

Abbaspour, Sara / Fallah, Ali / Lindén, Maria / Gholamhosseini, Hamid

Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology

2016 Volume 26, Page(s) 52–59

Abstract: In recent years, the removal of electrocardiogram (ECG) interferences from electromyogram (EMG) signals has been given large consideration. Where the quality of EMG signal is of interest, it is important to remove ECG interferences from EMG signals. In ... ...

Abstract	In recent years, the removal of electrocardiogram (ECG) interferences from electromyogram (EMG) signals has been given large consideration. Where the quality of EMG signal is of interest, it is important to remove ECG interferences from EMG signals. In this paper, an efficient method based on a combination of adaptive neuro-fuzzy inference system (ANFIS) and wavelet transform is proposed to effectively eliminate ECG interferences from surface EMG signals. The proposed approach is compared with other common methods such as high-pass filter, artificial neural network, adaptive noise canceller, wavelet transform, subtraction method and ANFIS. It is found that the performance of the proposed ANFIS-wavelet method is superior to the other methods with the signal to noise ratio and relative error of 14.97dB and 0.02 respectively and a significantly higher correlation coefficient (p<0.05).
MeSH term(s)	Electrocardiography/methods ; Electrocardiography/standards ; Electromyography/methods ; Electromyography/standards ; Humans ; Male ; Muscle, Skeletal/physiology ; Neural Networks (Computer) ; Signal-To-Noise Ratio ; Wavelet Analysis ; Young Adult
Language	English
Publishing date	2016-02
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	1073161-1
ISSN	1873-5711 ; 1050-6411
ISSN (online)	1873-5711
ISSN	1050-6411
DOI	10.1016/j.jelekin.2015.11.003
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Article ; Online: Evaluation of surface EMG-based recognition algorithms for decoding hand movements.

Abbaspour, Sara / Lindén, Maria / Gholamhosseini, Hamid / Naber, Autumn / Ortiz-Catalan, Max

Medical & biological engineering & computing

2019 Volume 58, Issue 1, Page(s) 83–100

Abstract: Myoelectric pattern recognition (MPR) to decode limb movements is an important advancement regarding the control of powered prostheses. However, this technology is not yet in wide clinical use. Improvements in MPR could potentially increase the ... ...

Abstract	Myoelectric pattern recognition (MPR) to decode limb movements is an important advancement regarding the control of powered prostheses. However, this technology is not yet in wide clinical use. Improvements in MPR could potentially increase the functionality of powered prostheses. To this purpose, offline accuracy and processing time were measured over 44 features using six classifiers with the aim of determining new configurations of features and classifiers to improve the accuracy and response time of prosthetics control. An efficient feature set (FS: waveform length, correlation coefficient, Hjorth Parameters) was found to improve the motion recognition accuracy. Using the proposed FS significantly increased the performance of linear discriminant analysis, K-nearest neighbor, maximum likelihood estimation (MLE), and support vector machine by 5.5%, 5.7%, 6.3%, and 6.2%, respectively, when compared with the Hudgins' set. Using the FS with MLE provided the largest improvement in offline accuracy over the Hudgins feature set, with minimal effect on the processing time. Among the 44 features tested, logarithmic root mean square and normalized logarithmic energy yielded the highest recognition rates (above 95%). We anticipate that this work will contribute to the development of more accurate surface EMG-based motor decoding systems for the control prosthetic hands.
MeSH term(s)	Adult ; Algorithms ; Electromyography ; Hand/physiology ; Humans ; Middle Aged ; Movement/physiology ; Principal Component Analysis ; Signal Processing, Computer-Assisted ; Time Factors ; Young Adult
Language	English
Publishing date	2019-11-21
Publishing country	United States
Document type	Evaluation Study ; Journal Article
ZDB-ID	282327-5
ISSN	1741-0444 ; 0025-696X ; 0140-0118
ISSN (online)	1741-0444
ISSN	0025-696X ; 0140-0118
DOI	10.1007/s11517-019-02073-z
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