Personal Health E-Record—Toward an Enabling Ambient Assisted Living Technology for Communication and Information Sharing Between Patients and Care Providers

Dimauro, Giovanni; Girardi, Francesco; Caivano, Danilo; Colizzi, Lucio

doi:10.1007/978-3-030-05921-7_39

Cited by 15 publications

(13 citation statements)

References 11 publications

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“…Then, the techniques described in this paper are considered to be extremely important, particularly for patients who need frequent blood tests or who have difficulty going frequently to test labs. More generally, this type of analysis is suitable in the increasingly frequent cases of managing patient diseases directly in their homes, through appropriate diagnostic and therapeutic care pathways and services such as medical records [23,24]. For this reason, it is of great interest to study methods and design tools that allow us to monitor the hemoglobin concentration in a non-invasive way, with reduced costs, both in the laboratory and at home.…”

Section: Introductionmentioning

confidence: 99%

Semantic Segmentation of Conjunctiva Region for Non-Invasive Anemia Detection Applications

Kasiviswanathan¹,

Bai²,

Simone

et al. 2020

Electronics

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Technology is changing the future of healthcare, technology-supported non-invasive medical procedures are more preferable in the medical diagnosis. Anemia is one of the widespread diseases affecting the wellbeing of individuals around the world especially childbearing age women and children and addressing this issue with the advanced technology will reduce the prevalence in large numbers. The objective of this work is to perform segmentation of the conjunctiva region for non-invasive anemia detection applications using deep learning. The proposed U-Net Based Conjunctiva Segmentation Model (UNBCSM) uses fine-tuned U-Net architecture for effective semantic segmentation of conjunctiva from the digital eye images captured by consumer-grade cameras in an uncontrolled environment. The ground truth for this supervised learning was given as Pascal masks obtained by manual selection of conjunctiva pixels. Image augmentation and pre-processing was performed to increase the data size and the performance of the model. UNBCSM showed good segmentation results and exhibited a comparable value of Intersection over Union (IoU) score between the ground truth and the segmented mask of 96% and 85.7% for training and validation, respectively.

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Section: Introductionmentioning

confidence: 99%

Semantic Segmentation of Conjunctiva Region for Non-Invasive Anemia Detection Applications

Kasiviswanathan¹,

Bai²,

Simone

et al. 2020

Electronics

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“…Non-invasive approaches for estimating anemia or hemoglobin concentration level are considered to be extremely important, particularly for patients who need frequent blood tests or who have difficulty going frequently to test labs. More generally, this type of analysis is suitable in the increasingly frequent cases of managing patient diseases directly in their homes, through appropriate diagnostic and therapeutic care pathways and services like medical records [30,31].…”

Section: Digital Analysis Of Images Of Exposed Human Tissues Based Onmentioning

confidence: 99%

Estimate of Anemia with New Non-Invasive Systems—A Moment of Reflection

et al. 2020

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Anemia is a global public health problem with major consequences for human health. About a quarter of the world population shows a hemoglobin concentration that is below the recommended thresholds. Non-invasive methods for monitoring and identifying potential risk of anemia and smartphone-based devices to perform this task are promising in addressing this pathology. We have considered some well-known studies carried out on this topic since the main purpose of this work was not to produce a review. The first group of papers describes the approaches for the clinical evaluation of anemia focused on different human exposed tissues, while we used a second group to overview some technologies, basic methods, and principles of operation of some devices and highlight some technical problems. Results extracted from the second group of papers examined were aggregated in two comparison tables. A growing interest in this topic is demonstrated by the increasing number of papers published recently. We believe we have identified several critical issues in the published studies, including those published by us. Just as an example, in many papers the dataset used is not described. With this paper we wish to open a discussion on these issues. Few papers have been sufficient to highlight differences in the experimental conditions and this makes the comparison of the results difficult. Differences are also found in the identification of the regions of interest in the tissue, descriptions of the datasets, and other boundary conditions. These critical issues are discussed together with open problems and common mistakes that probably we are making. We propose the definition of a road-map and a common agenda for research on this topic. In this sense, we want to highlight here some issues that seem worthy of common discussion and the subject of synergistic agreements. This paper, and in particular, the discussion could be the starting point for an open debate about the dissemination of our experiments and pave the way for further updates and improvements of what we have outlined.

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“…Therefore, high expectations are placed in the use of such techniques also for the improvement of health care and clinical practice [ 31 , 32 , 33 , 34 , 35 , 36 ]. Furthermore, numerous portable devices for personal and frequent monitoring of cardiac activity, such as Kardia [ 37 ], D-hearth [ 38 ], and eKuore [ 39 ], are spreading.…”

Section: Introductionmentioning

confidence: 99%

Personal Heart Health Monitoring Based on 1D Convolutional Neural Network

et al. 2021

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The automated detection of suspicious anomalies in electrocardiogram (ECG) recordings allows frequent personal heart health monitoring and can drastically reduce the number of ECGs that need to be manually examined by the cardiologists, excluding those classified as normal, facilitating healthcare decision-making and reducing a considerable amount of time and money. In this paper, we present a system able to automatically detect the suspect of cardiac pathologies in ECG signals from personal monitoring devices, with the aim to alert the patient to send the ECG to the medical specialist for a correct diagnosis and a proper therapy. The main contributes of this work are: (a) the implementation of a binary classifier based on a 1D-CNN architecture for detecting the suspect of anomalies in ECGs, regardless of the kind of cardiac pathology; (b) the analysis was carried out on 21 classes of different cardiac pathologies classified as anomalous; and (c) the possibility to classify anomalies even in ECG segments containing, at the same time, more than one class of cardiac pathologies. Moreover, 1D-CNN based architectures can allow an implementation of the system on cheap smart devices with low computational complexity. The system was tested on the ECG signals from the MIT-BIH ECG Arrhythmia Database for the MLII derivation. Two different experiments were carried out, showing remarkable performance compared to other similar systems. The best result showed high accuracy and recall, computed in terms of ECG segments and even higher accuracy and recall in terms of patients alerted, therefore considering the detection of anomalies with respect to entire ECG recordings.

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Personal Health E-Record—Toward an Enabling Ambient Assisted Living Technology for Communication and Information Sharing Between Patients and Care Providers

Abstract: The audience for the books in LNEE consists of advanced level students, researchers, and industry professionals working at the forefront of their fields. Much like Springer's other Lecture Notes series, LNEE will be distributed through Springer's print and electronic publishing channels.

Cited by 15 publications

References 11 publications

Semantic Segmentation of Conjunctiva Region for Non-Invasive Anemia Detection Applications

Semantic Segmentation of Conjunctiva Region for Non-Invasive Anemia Detection Applications

Estimate of Anemia with New Non-Invasive Systems—A Moment of Reflection

Personal Heart Health Monitoring Based on 1D Convolutional Neural Network

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