Pub Date : 2017-12-19DOI: 10.1109/HIC.2017.8227579
Che-Wei Lin, Yung-Cheng Chang, Chou-Ching K. Lin, L. Tsai, Ju-Yi Chen
An artificial intelligence-based (AI-based) noninvasive Pulse AudioGram (PAG) monitoring device with arrhythmia screening algorithm has been developed in this research study. The PAG monitoring device consists of four components, including an audiogram sensor, an analog-digital converter, a microprocessor, and a data storage unit. The main function of the proposed AI-based non-invasive PAG is to measure the audio signal in radial artery generated by hemodynamics. Hemodynamics under arrhythmia and sinus rhythm (SR) conditions might exhibit different patterns as the heart rhythm becomes irregular under arrhythmia condition. PAG signals of SR and other arrhythmia symptoms such as atrial fibrillation (AF), aortic regurgitation (AR), and congestive heart failure (CHF) were collected during this research. In the experiment results, the proposed method can achieve accuracy of 99.29% when discriminating SR and AF; the proposed method can achieve accuracy of 98.92% when discriminating SR, AF, AR, and CHF. In this study, we have successfully developed an AI-based non-invasive PAG monitoring device for arrhythmia screening, and have plan to use it in on large-scale screening for arrhythmia in the near future.
{"title":"Development of an AI-based non-invasive Pulse AudioGram monitoring device for arrhythmia screening","authors":"Che-Wei Lin, Yung-Cheng Chang, Chou-Ching K. Lin, L. Tsai, Ju-Yi Chen","doi":"10.1109/HIC.2017.8227579","DOIUrl":"https://doi.org/10.1109/HIC.2017.8227579","url":null,"abstract":"An artificial intelligence-based (AI-based) noninvasive Pulse AudioGram (PAG) monitoring device with arrhythmia screening algorithm has been developed in this research study. The PAG monitoring device consists of four components, including an audiogram sensor, an analog-digital converter, a microprocessor, and a data storage unit. The main function of the proposed AI-based non-invasive PAG is to measure the audio signal in radial artery generated by hemodynamics. Hemodynamics under arrhythmia and sinus rhythm (SR) conditions might exhibit different patterns as the heart rhythm becomes irregular under arrhythmia condition. PAG signals of SR and other arrhythmia symptoms such as atrial fibrillation (AF), aortic regurgitation (AR), and congestive heart failure (CHF) were collected during this research. In the experiment results, the proposed method can achieve accuracy of 99.29% when discriminating SR and AF; the proposed method can achieve accuracy of 98.92% when discriminating SR, AF, AR, and CHF. In this study, we have successfully developed an AI-based non-invasive PAG monitoring device for arrhythmia screening, and have plan to use it in on large-scale screening for arrhythmia in the near future.","PeriodicalId":120815,"journal":{"name":"2017 IEEE Healthcare Innovations and Point of Care Technologies (HI-POCT)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126361736","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-12-19DOI: 10.1109/HIC.2017.8227587
Muhammad S. Khan, K. Dighe, Zhen Wang, E. Daza, A. Schwartz-Duval, Conor P. Rowley, Isaac A. Calvillo, Dr. Santoshi Misra, Leanne T. Labriola, D. Pan
In this paper, we present a biosensor chip to study two important biomarkers, lactoferrin (LCF) and beta-2-microglubin (B2M) present in our tear fluid. LCF and B2M have direct relevance with dry eye syndrome and diabetic retinopathy in patients respectively. We have developed a multi-layer paper-based biosensor chip connected with handheld printed circuit board (PCB) to monitor the effectiveness of B2M and LCF in contrived tear film (TF) with a wide range of detection (RoD) from 0.001 to 1ng/mL (R2=0.9886) and 1 to 10mg/mL (R2=0.9554) respectively. Multilayer biosensor is composed of graphene nanoplatelet and diblock-co-polymer coated on a filter paper followed by deposition of micro-Au electrodes. Anti-LCF and anti-B2M antibodies were immobilized via 3,3'-dithiodipropionic acid di(N-hydroxysuccinimide ester (DTSP) as a self-assembled monolayer (SAM) on Au. In addition to electrical measurements in real-time setup, Raman spectral analysis and SEM were utilized to characterize the sensor surface before and after testing the relevant proteins. We anticipate that the fabricated sensor chip has promising application to study dry-eye-disease (DED) and diabetes in Point-of-care (POC) setting.
{"title":"Label-free detection of lactoferrin and beta-2-microglobuin in contrived tear film using a low-cost electrical biosensor chip","authors":"Muhammad S. Khan, K. Dighe, Zhen Wang, E. Daza, A. Schwartz-Duval, Conor P. Rowley, Isaac A. Calvillo, Dr. Santoshi Misra, Leanne T. Labriola, D. Pan","doi":"10.1109/HIC.2017.8227587","DOIUrl":"https://doi.org/10.1109/HIC.2017.8227587","url":null,"abstract":"In this paper, we present a biosensor chip to study two important biomarkers, lactoferrin (LCF) and beta-2-microglubin (B2M) present in our tear fluid. LCF and B2M have direct relevance with dry eye syndrome and diabetic retinopathy in patients respectively. We have developed a multi-layer paper-based biosensor chip connected with handheld printed circuit board (PCB) to monitor the effectiveness of B2M and LCF in contrived tear film (TF) with a wide range of detection (RoD) from 0.001 to 1ng/mL (R2=0.9886) and 1 to 10mg/mL (R2=0.9554) respectively. Multilayer biosensor is composed of graphene nanoplatelet and diblock-co-polymer coated on a filter paper followed by deposition of micro-Au electrodes. Anti-LCF and anti-B2M antibodies were immobilized via 3,3'-dithiodipropionic acid di(N-hydroxysuccinimide ester (DTSP) as a self-assembled monolayer (SAM) on Au. In addition to electrical measurements in real-time setup, Raman spectral analysis and SEM were utilized to characterize the sensor surface before and after testing the relevant proteins. We anticipate that the fabricated sensor chip has promising application to study dry-eye-disease (DED) and diabetes in Point-of-care (POC) setting.","PeriodicalId":120815,"journal":{"name":"2017 IEEE Healthcare Innovations and Point of Care Technologies (HI-POCT)","volume":"117 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123732751","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-11-01DOI: 10.1109/HIC.2017.8227631
Meilin Zhu, U. Obahiagbon, K. Anderson, J. Christen
Point-of-Care (PoC) diagnostic devices, such as lateral flow tests, are often used in low and middle-income countries (LMIC) for low-cost disease detection. Most commercial lateral flow tests use colorimetric detection on a nitrocellulose substrate. In this paper, we present a multistep, fluorescence-based assay detection system, which can detect antibodies in plasma to recombinant protein. Fluorescence-based detection allows us to achieve higher sensitivity, while a nitrocellulose substrate enables fluid handling, high protein immobilization, rapid detection time, and affordability. As a proof-of-concept for detection of disease-specific biomarkers in plasma, we demonstrate the detection of antibodies in plasma to Epstein-Barr nuclear antigen-1 (EBNA-1) recombinant protein and to human papillomavirus (HPV) 16 E7 recombinant protein. We show that our detection system is able to detect EBNA-1-specific antibodies at a 1:10,000 plasma dilution and HPV 16 E7-specific antibodies at a 1:5,000 plasma dilution, indicating high sensitivity. This platform is a low-cost device that can detect fluorescence from labeled biomarkers on a lateral flow assay. Ultimately, we aim to adapt this system to detect HPV 16 and 18 biomarkers for cervical cancer screening in LMICs.
{"title":"Highly sensitive fluorescence-based lateral flow platform for point-of-care detection of biomarkers in plasma","authors":"Meilin Zhu, U. Obahiagbon, K. Anderson, J. Christen","doi":"10.1109/HIC.2017.8227631","DOIUrl":"https://doi.org/10.1109/HIC.2017.8227631","url":null,"abstract":"Point-of-Care (PoC) diagnostic devices, such as lateral flow tests, are often used in low and middle-income countries (LMIC) for low-cost disease detection. Most commercial lateral flow tests use colorimetric detection on a nitrocellulose substrate. In this paper, we present a multistep, fluorescence-based assay detection system, which can detect antibodies in plasma to recombinant protein. Fluorescence-based detection allows us to achieve higher sensitivity, while a nitrocellulose substrate enables fluid handling, high protein immobilization, rapid detection time, and affordability. As a proof-of-concept for detection of disease-specific biomarkers in plasma, we demonstrate the detection of antibodies in plasma to Epstein-Barr nuclear antigen-1 (EBNA-1) recombinant protein and to human papillomavirus (HPV) 16 E7 recombinant protein. We show that our detection system is able to detect EBNA-1-specific antibodies at a 1:10,000 plasma dilution and HPV 16 E7-specific antibodies at a 1:5,000 plasma dilution, indicating high sensitivity. This platform is a low-cost device that can detect fluorescence from labeled biomarkers on a lateral flow assay. Ultimately, we aim to adapt this system to detect HPV 16 and 18 biomarkers for cervical cancer screening in LMICs.","PeriodicalId":120815,"journal":{"name":"2017 IEEE Healthcare Innovations and Point of Care Technologies (HI-POCT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126971171","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-11-01DOI: 10.1109/HIC.2017.8227610
M. N. Hasan, A. Fraiwan, J. Little, U. Gurkan
Sickle Cell Disease (SCD) is a genetically inherited hemoglobin disorder, which can be fatal if left undiagnosed and untreated. Geographically, the most SCD-prevalent regions have the lowest gross domestic product (GDP) and are therefore unable to implement costly, centralized SCD screening programs. In these regions, the early mortality is 50%–90% among children born with sickle cell anemia [1]. According to the World Health Organization (WHO), who passed a resolution naming SCD as a global public health problem in 2006 [2], 70% of these early mortality could be prevented by implementing low-cost SCD screening followed by cost-effective treatments [3]. To address this need, we developed HemeChip, a mass-producible, low-cost, microchip version of electrophoresis, able to detect and quantify hemoglobin type(s) from whole blood at the point of care (POC). The process is fast (<10 minutes), efficient, and can be performed by minimally trained personnel.
{"title":"A low-cost, mass-producible point-of-care platform for diagnosing hemoglobin disorders","authors":"M. N. Hasan, A. Fraiwan, J. Little, U. Gurkan","doi":"10.1109/HIC.2017.8227610","DOIUrl":"https://doi.org/10.1109/HIC.2017.8227610","url":null,"abstract":"Sickle Cell Disease (SCD) is a genetically inherited hemoglobin disorder, which can be fatal if left undiagnosed and untreated. Geographically, the most SCD-prevalent regions have the lowest gross domestic product (GDP) and are therefore unable to implement costly, centralized SCD screening programs. In these regions, the early mortality is 50%–90% among children born with sickle cell anemia [1]. According to the World Health Organization (WHO), who passed a resolution naming SCD as a global public health problem in 2006 [2], 70% of these early mortality could be prevented by implementing low-cost SCD screening followed by cost-effective treatments [3]. To address this need, we developed HemeChip, a mass-producible, low-cost, microchip version of electrophoresis, able to detect and quantify hemoglobin type(s) from whole blood at the point of care (POC). The process is fast (<10 minutes), efficient, and can be performed by minimally trained personnel.","PeriodicalId":120815,"journal":{"name":"2017 IEEE Healthcare Innovations and Point of Care Technologies (HI-POCT)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121214101","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-11-01DOI: 10.1109/HIC.2017.8227621
Hamza Abujrida, E. Agu, K. Pahlavan
People afflicted with Parkinson's Disease (PD) experience impairment of their gait (the way a person walks), which frequently results in falls. In this paper we investigate a machine learning method to assess PD severity using accelerometer data passively crowdsourced from participants' smartphones while they walked. Time and frequency domain features such as entropy rate and peak frequency, and postural sway features were extracted from accelerometer data and classified. Our work is the first to classify PD severity on the UPDRS scale and distinguish PD patients from controls, using noisy crowdsourced data. Our crowdsourcing approach examined 50 patients in the wild, demonstrating the potential to use smartphone sensing to remotely assess and monitor PD patients at the population level. The random forest classifier was the most accurate in distinguishing subjects from controls with an average accuracy of 87.03% and also for assessing PD severity (Normal, Slight, Mild, Moderate and Severe), with an average accuracy of 85.8%.
{"title":"Smartphone-based gait assessment to infer Parkinson's disease severity using crowdsourced data","authors":"Hamza Abujrida, E. Agu, K. Pahlavan","doi":"10.1109/HIC.2017.8227621","DOIUrl":"https://doi.org/10.1109/HIC.2017.8227621","url":null,"abstract":"People afflicted with Parkinson's Disease (PD) experience impairment of their gait (the way a person walks), which frequently results in falls. In this paper we investigate a machine learning method to assess PD severity using accelerometer data passively crowdsourced from participants' smartphones while they walked. Time and frequency domain features such as entropy rate and peak frequency, and postural sway features were extracted from accelerometer data and classified. Our work is the first to classify PD severity on the UPDRS scale and distinguish PD patients from controls, using noisy crowdsourced data. Our crowdsourcing approach examined 50 patients in the wild, demonstrating the potential to use smartphone sensing to remotely assess and monitor PD patients at the population level. The random forest classifier was the most accurate in distinguishing subjects from controls with an average accuracy of 87.03% and also for assessing PD severity (Normal, Slight, Mild, Moderate and Severe), with an average accuracy of 85.8%.","PeriodicalId":120815,"journal":{"name":"2017 IEEE Healthcare Innovations and Point of Care Technologies (HI-POCT)","volume":"95 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125700572","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-11-01DOI: 10.1109/HIC.2017.8227589
Tanmay Kulkarni, G. Slaughter
We describe herein the construction of a glucose biosensor system that senses glucose without the use of potentiostat and harness usable electrical energy from glucose to power a digital electronic device simultaneously. This system is powered by a single enzymatic glucose biofuel cell comprising a highly dense mesh network of multi-walled carbon nanotubes and enzymes that results in the immobilization and direct electrical connection of the enzymes. The bioanode is functionalized with pyroloquinoline quinone glucose dehydrogenase, while the biocathode is functionalized with bilirubin oxidase. The glucose biofuel cell assembly and its performance were investigated under physiological conditions (pH 7.4 and 37 °C) at which a maximum open circuit voltage of 548.2 mV, short circuit current density of 7.19 mA/cm2 and peak power density of 1.475 mA/cm2 were obtained in 20 mM glucose. The nominal electrical power generated from the single glucose biofuel cell (∼ 548 mV) was amplified to 3.2 V using a two stage electrical power amplification circuit and a capacitive element functioning as a glucose transducer. The self-powered glucose biosensor exhibited a linear dynamic range of 1 mM–45 mM and a high sensitivity of 92.51 Hz/cm2.mM when simultaneously sensing glucose and powering a glucometer. Additionally, the system demonstrated excellent operational stability over a 53-day period.
{"title":"Simultaneous glucose sensing and bioenergy harnessing","authors":"Tanmay Kulkarni, G. Slaughter","doi":"10.1109/HIC.2017.8227589","DOIUrl":"https://doi.org/10.1109/HIC.2017.8227589","url":null,"abstract":"We describe herein the construction of a glucose biosensor system that senses glucose without the use of potentiostat and harness usable electrical energy from glucose to power a digital electronic device simultaneously. This system is powered by a single enzymatic glucose biofuel cell comprising a highly dense mesh network of multi-walled carbon nanotubes and enzymes that results in the immobilization and direct electrical connection of the enzymes. The bioanode is functionalized with pyroloquinoline quinone glucose dehydrogenase, while the biocathode is functionalized with bilirubin oxidase. The glucose biofuel cell assembly and its performance were investigated under physiological conditions (pH 7.4 and 37 °C) at which a maximum open circuit voltage of 548.2 mV, short circuit current density of 7.19 mA/cm2 and peak power density of 1.475 mA/cm2 were obtained in 20 mM glucose. The nominal electrical power generated from the single glucose biofuel cell (∼ 548 mV) was amplified to 3.2 V using a two stage electrical power amplification circuit and a capacitive element functioning as a glucose transducer. The self-powered glucose biosensor exhibited a linear dynamic range of 1 mM–45 mM and a high sensitivity of 92.51 Hz/cm2.mM when simultaneously sensing glucose and powering a glucometer. Additionally, the system demonstrated excellent operational stability over a 53-day period.","PeriodicalId":120815,"journal":{"name":"2017 IEEE Healthcare Innovations and Point of Care Technologies (HI-POCT)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134325917","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-11-01DOI: 10.1109/HIC.2017.8227598
Jenna L. Mueller, Christopher T Lam, M. Kellish, Jenna Peters, M. Asiedu, Marlee S. Krieger, Jennifer E. Gallagher, A. Erkanli, Ernesto J Ortiz, Lisa C. Muasher, Peyton Taylor, B. Mchome, O. Oneko, Gino Venegas, John W Schmitt, N. Ramanujam
Cervical cancer is the second leading cause of cancer related deaths for women worldwide, despite being both preventable and treatable if detected early enough. The burden of disease persists in part due to a lack of access to early diagnostics and the fact that a significant proportion of women do not return for follow-up care. To address this need, we developed a low-cost, portable Pocket colposcope that can be used in remote settings for cervical cancer screening and prevention. We evaluated whether the Pocket colposcope performs comparably to the standard-of-care colposcope in Durham, North Carolina, Lima, Perú, and Moshi, Tanzania. Concordant image pairs obtained from this device and the current standard imaging was sent to physician experts from high, middle, and low-income countries. They were blinded as to diagnosis, device, and peer response. Each image was critically evaluated for quality and presumptive findings and compared. Corresponding pathology was obtained for each patient. Physician interpretation agreed 76.8% of the time between the Pocket colposcope and standard-of-care colposcope. Physician interpretation agreed with pathology 59.4% and 63.0% for the Pocket and standard-of-care colposcopes, respectively. The primary outcome of this study was to show reasonable concordance between the Pocket colposcope and state of the art imaging in terms of individual examiners, pathologic diagnosis, and image quality. In all areas the Pocket colposcope showed comparable results to country-specific standard-of-care screening.
子宫颈癌是全世界妇女癌症相关死亡的第二大原因，尽管如果发现得足够早，它既可以预防，也可以治疗。疾病负担持续存在的部分原因是无法获得早期诊断，而且很大一部分妇女没有返回接受后续护理。为了满足这一需求，我们开发了一种低成本的便携式口袋阴道镜，可以在偏远地区用于宫颈癌筛查和预防。我们评估了Pocket阴道镜是否与Durham, North Carolina, Lima, Perú和Moshi, Tanzania的标准护理阴道镜相比较。从该设备获得的与现行标准成像一致的图像对被发送给来自高、中、低收入国家的医师专家。他们对诊断、设备和同伴反应一无所知。每张图像的质量和推定结果进行了严格的评估和比较。对每位患者进行相应的病理检查。医师解释在口袋阴道镜和标准护理阴道镜之间的一致性为76.8%。Pocket阴道镜和标准护理阴道镜的医师解释与病理的一致性分别为59.4%和63.0%。本研究的主要结果是在个体检查、病理诊断和图像质量方面显示口袋阴道镜与最先进的成像技术之间的合理一致性。在所有地区，口袋阴道镜显示出与国家特定标准护理筛查相当的结果。
{"title":"Clinical evaluation of a portable pocket colposcope for cervical cancer screening in the United States, Perú, and Tanzania","authors":"Jenna L. Mueller, Christopher T Lam, M. Kellish, Jenna Peters, M. Asiedu, Marlee S. Krieger, Jennifer E. Gallagher, A. Erkanli, Ernesto J Ortiz, Lisa C. Muasher, Peyton Taylor, B. Mchome, O. Oneko, Gino Venegas, John W Schmitt, N. Ramanujam","doi":"10.1109/HIC.2017.8227598","DOIUrl":"https://doi.org/10.1109/HIC.2017.8227598","url":null,"abstract":"Cervical cancer is the second leading cause of cancer related deaths for women worldwide, despite being both preventable and treatable if detected early enough. The burden of disease persists in part due to a lack of access to early diagnostics and the fact that a significant proportion of women do not return for follow-up care. To address this need, we developed a low-cost, portable Pocket colposcope that can be used in remote settings for cervical cancer screening and prevention. We evaluated whether the Pocket colposcope performs comparably to the standard-of-care colposcope in Durham, North Carolina, Lima, Perú, and Moshi, Tanzania. Concordant image pairs obtained from this device and the current standard imaging was sent to physician experts from high, middle, and low-income countries. They were blinded as to diagnosis, device, and peer response. Each image was critically evaluated for quality and presumptive findings and compared. Corresponding pathology was obtained for each patient. Physician interpretation agreed 76.8% of the time between the Pocket colposcope and standard-of-care colposcope. Physician interpretation agreed with pathology 59.4% and 63.0% for the Pocket and standard-of-care colposcopes, respectively. The primary outcome of this study was to show reasonable concordance between the Pocket colposcope and state of the art imaging in terms of individual examiners, pathologic diagnosis, and image quality. In all areas the Pocket colposcope showed comparable results to country-specific standard-of-care screening.","PeriodicalId":120815,"journal":{"name":"2017 IEEE Healthcare Innovations and Point of Care Technologies (HI-POCT)","volume":"86 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124267811","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-11-01DOI: 10.1109/HIC.2017.8227575
P. C. Adithya, S. Pandey, R. Sankar, S. Hart, W. Moreno
This study presents a cluster analysis framework for acoustic signals of the novel catheter stethoscope. The objective of the current study is to collect the blood flow sounds from a body site of a Yorkshire pig using the novel acoustic catheter stethoscope and further recognize any changes in the sinus rhythm patterns. Initially the collected blood flow sounds are preprocessed with noise cancellation and wavelet source separation to result in acoustic heart and respiratory pulses. Then, the extracted acoustic heart pulses are post processed to recognize the patterns of the sinus rhythm based on a novel feature extraction technique and cluster analysis. Finally, the developed framework is qualitatively and quantitatively validated by providing visual results of the clustering and by computing the clustering accuracy, sensitivity and specificity. The cross validation results show that the developed framework consistently recognizes the patterns of the sinus rhythms with an accuracy rate of 94.37%.
{"title":"Cluster analysis framework for novel acoustic catheter stethoscope","authors":"P. C. Adithya, S. Pandey, R. Sankar, S. Hart, W. Moreno","doi":"10.1109/HIC.2017.8227575","DOIUrl":"https://doi.org/10.1109/HIC.2017.8227575","url":null,"abstract":"This study presents a cluster analysis framework for acoustic signals of the novel catheter stethoscope. The objective of the current study is to collect the blood flow sounds from a body site of a Yorkshire pig using the novel acoustic catheter stethoscope and further recognize any changes in the sinus rhythm patterns. Initially the collected blood flow sounds are preprocessed with noise cancellation and wavelet source separation to result in acoustic heart and respiratory pulses. Then, the extracted acoustic heart pulses are post processed to recognize the patterns of the sinus rhythm based on a novel feature extraction technique and cluster analysis. Finally, the developed framework is qualitatively and quantitatively validated by providing visual results of the clustering and by computing the clustering accuracy, sensitivity and specificity. The cross validation results show that the developed framework consistently recognizes the patterns of the sinus rhythms with an accuracy rate of 94.37%.","PeriodicalId":120815,"journal":{"name":"2017 IEEE Healthcare Innovations and Point of Care Technologies (HI-POCT)","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128195857","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-11-01DOI: 10.1109/HIC.2017.8227623
S. Gill, Jason Hearn, Graeme Powell, E. Scheme
The increasing stress on the global healthcare system driven by the rise of chronic disease and an aging population is necessitating an emphasis on proactive health monitoring and self-management. Potential exists in the wave of emerging wearable devices and the internet of things (IoT) to support a movement towards the decentralization of healthcare. In particular, mobility impairments caused by injury or chronic disease are a major source of concern in the aging population. Individuals with mobility impairments often rely on assistive devices, such as canes or walkers to increase safety and stability. Given the prevalence of assistive devices among these users, instrumenting and connecting assistive technologies could be an effective means of unobtrusive activity monitoring. In this work we present an affordable hybrid sensorized cane, capable of measuring loading, mobility and stability information. The proposed system, which is nearly indistinguishable from a traditional cane, collects these data and then wirelessly transmits them to a mobile device for cloud storage and analysis. A multi-sensor fusion algorithm was used to segment valid gait cycles and identify various temporal gait events. Based on preliminary results, it is believed that the proposed system will be able to identify a variety of gait perturbations, potentially offering future applications in early diagnosis and the management of chronic conditions.
{"title":"Design of a multi-sensor IoT-enabled assistive device for discrete and deployable gait monitoring","authors":"S. Gill, Jason Hearn, Graeme Powell, E. Scheme","doi":"10.1109/HIC.2017.8227623","DOIUrl":"https://doi.org/10.1109/HIC.2017.8227623","url":null,"abstract":"The increasing stress on the global healthcare system driven by the rise of chronic disease and an aging population is necessitating an emphasis on proactive health monitoring and self-management. Potential exists in the wave of emerging wearable devices and the internet of things (IoT) to support a movement towards the decentralization of healthcare. In particular, mobility impairments caused by injury or chronic disease are a major source of concern in the aging population. Individuals with mobility impairments often rely on assistive devices, such as canes or walkers to increase safety and stability. Given the prevalence of assistive devices among these users, instrumenting and connecting assistive technologies could be an effective means of unobtrusive activity monitoring. In this work we present an affordable hybrid sensorized cane, capable of measuring loading, mobility and stability information. The proposed system, which is nearly indistinguishable from a traditional cane, collects these data and then wirelessly transmits them to a mobile device for cloud storage and analysis. A multi-sensor fusion algorithm was used to segment valid gait cycles and identify various temporal gait events. Based on preliminary results, it is believed that the proposed system will be able to identify a variety of gait perturbations, potentially offering future applications in early diagnosis and the management of chronic conditions.","PeriodicalId":120815,"journal":{"name":"2017 IEEE Healthcare Innovations and Point of Care Technologies (HI-POCT)","volume":"153 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127274985","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-11-01DOI: 10.1109/HIC.2017.8227572
Long Jiang, Shikui Chen, C. Sadasivan, X. Jiao
In this paper, the 3D structural topology optimization is performed to create an innovative design of the aneurysm implant. The topology optimization scheme is utilized to optimize the geometry of the implant structure to satisfy both the fluidic and the structural design objectives. During the topology optimization process, an artificial density is used as the index to separate the material region from the void region. The goal of the implant structure design is expected to create a “fluid diode” effect while to possess enough stiffness to withstand the outside pressure. The final design is printed by additive manufacturing to validate the performance. Both numerical verification and experimental validation proved the effectiveness of the proposed implant structure under the current problem settings. This work showed the potential of topology optimization as a powerful tool in producing innovative high-performance implant designs for aneurysm treatments, which will pave the way for personalized medical implants.
{"title":"Structural topology optimization for generative design of personalized aneurysm implants: Design, additive manufacturing, and experimental validation","authors":"Long Jiang, Shikui Chen, C. Sadasivan, X. Jiao","doi":"10.1109/HIC.2017.8227572","DOIUrl":"https://doi.org/10.1109/HIC.2017.8227572","url":null,"abstract":"In this paper, the 3D structural topology optimization is performed to create an innovative design of the aneurysm implant. The topology optimization scheme is utilized to optimize the geometry of the implant structure to satisfy both the fluidic and the structural design objectives. During the topology optimization process, an artificial density is used as the index to separate the material region from the void region. The goal of the implant structure design is expected to create a “fluid diode” effect while to possess enough stiffness to withstand the outside pressure. The final design is printed by additive manufacturing to validate the performance. Both numerical verification and experimental validation proved the effectiveness of the proposed implant structure under the current problem settings. This work showed the potential of topology optimization as a powerful tool in producing innovative high-performance implant designs for aneurysm treatments, which will pave the way for personalized medical implants.","PeriodicalId":120815,"journal":{"name":"2017 IEEE Healthcare Innovations and Point of Care Technologies (HI-POCT)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122367349","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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