Developing wireless ECG device using Bluetooth protocol for interfacing with Android based applications

The current project involves the development of a wireless ECG recorder and an android application that can be integrated smoothly for use by health-conscious people or cardiac patients and clinicians as well for self-monitoring and feedback respectively. Briefly, the hardware as well as software for a wireless ECG device were designed and developed that was integrated with Android based application to display and records the user's ECG signal. The hardware incorporated microcontroller, MSP-430 that detected the cardiac signal and performed analog to digital conversion (ADC), digital filtering, QRS complex extraction and heart rate calculation using specific algorithms. During validation, the prototype successfully recorded the cardiac electric signals originated by subject’s heart with distinct QRS complex and T peaks. However, a distinct P wave was lacking. Further, the signal was rectified and the background noise was amplified followed by amplifying the signal to a tractable level. Next, the signal was communicated to an android device using Bluetooth protocol by HC-05 Bluetooth module. The signal was successfully displayed graphically, and the data was stored after being digitalized for future referencing and processing using advanced algorithms. The developed prototype is a robust, accurate and low-cost ECG recorder with wireless signal transmission to android device. The hardware incorporates distinct filter and amplification system to eliminate artifact from active movement. The use of adaptive filter is proposed for possible future improvement, with the main goal being to build the amplification and filter system which communicates with an Android smartphone application

Exploring Biosignals for Quantitative Pain Assessment in Cancer Patients: A Proof of Concept

Perception and expression of pain in cancer patients are influenced by distress levels, tumor type and progression, and the underlying pathophysiology of pain. Relying on traditional pain assessment tools can present limitations due to the highly subjective and multifaceted nature of the symptoms. In this scenario, objective pain assessment is an open research challenge. This work introduces a framework for automatic pain assessment. The proposed method is based on a wearable biosignal platform to extract quantitative indicators of the patient pain experience, evaluated through a self-assessment report. Two preliminary case studies focused on the simultaneous acquisition of electrocardiography (ECG), electrodermal activity (EDA), and accelerometer signals are illustrated and discussed. The results demonstrate the feasibility of the approach, highlighting the potential of EDA in capturing skin conductance responses (SCR) related to pain events in chronic cancer pain. A weak correlation (R = 0.2) is found between SCR parameters and the standard deviation of the interbeat interval series (SDRR), selected as the Heart Rate Variability index. A statistically significant (p < 0.001) increase in both EDA signal and SDRR is detected in movement with respect to rest conditions (assessed by means of the accelerometer signals) in the case of motion-associated cancer pain, thus reflecting the relationship between motor dynamics, which trigger painful responses, and the subsequent activation of the autonomous nervous system. With the objective of integrating parameters obtained from biosignals to establish pain signatures within different clinical scenarios, the proposed framework proves to be a promising research approach to define pain signatures in different clinical contexts

Case study: IBM Watson Analytics cloud platform as Analytics-as-a-Service system for heart failure early detection

In the recent years the progress in technology and the increasing availability of fast connections have produced a migration of functionalities in Information Technologies services, from static servers to distributed technologies. This article describes the main tools available on the market to perform Analytics as a Service (AaaS) using a cloud platform. It is also described a use case of IBM Watson Analytics, a cloud system for data analytics, applied to the following research scope: detecting the presence or absence of Heart Failure disease using nothing more than the electrocardiographic signal, in particular through the analysis of Heart Rate Variability. The obtained results are comparable with those coming from the literature, in terms of accuracy and predictive power. Advantages and drawbacks of cloud versus static approaches are discussed in the last sections

Personalized wearable systems for real-time ECG classification and healthcare interoperability: Real-time ECG classification and FHIR interoperability

Continuous monitoring of an individual's health using wearable biomedical devices is becoming a norm these days with a large number of wearable kits becoming easily available. Modern wearable health monitoring devices have become easily available in the consumer market, however, real-time analyses and prediction along with alerts and alarms about a health hazard are not adequately addressed in such devices. Taking ECG monitoring as a case study the research paper focusses on signal processing, arrhythmia detection and classification and at the same time focusses on updating the electronic health records database in realtime such that the concerned medical practitioners become aware of an emergent situation the patient being monitored might face. Also, heart rate variability (HRV) analysis is usually considered as a basis for arrhythmia classification which largely depends on the morphology of the ECG waveforms and the sensitivity of the biopotential measurements of the ECG kits, so it may not yield accurate results. Initially, the ECG readings from the 3-Lead ECG analog front-end were de-noised, zero-offset corrected, filtered using recursive least square adaptive filter and smoothed using Savitzky-Golay filter and subsequently passed to the data analysis component with a unique feature extraction method to increase the accuracy of classification. The machine learning models trained on MITDB arrhythmia database (MIT-BIH Physionet) showed more than 97% accuracy using kNN classifiers. Neuralnet fitting models showed mean-squared error of as low as 0.0085 and regression value as high as 0.99. ECG abnormalities based on annotations in MITDB could be classified and these ECG observations could be logged to a server implementation based on FHIR standards. The instruments were networked using IoT (Internet of Things) devices and ECG event observations were coded according to SNOMED coding system and could be accessed in Electronic Health Record by the concerned medic to - ake appropriate and timely decisions. The system emphasizes on `preventive care rather than remedial cure' as the next generation personalized health-care monitoring devices become available

Physiologic Effect of Relaxation Therapies on Autonomic Tone Early After Acute Coronary Syndromes

Post-MI patients are at increased risk of arrhythmic sudden death. Stress and sympathetic activation are known to influence arrhythmogenesis. While relaxation therapies improve psychological well-being in multiple medical illnesses, whether these therapies can positively influence sympathovagal balance in the post-MI population is unknown. We explored the physiologic effects of Reiki, a light-touch relaxation therapy, and music on post-acute coronary syndrome (ACS) inpatients, using heart rate variability (HRV) to assess changes in cardiac autonomic function during treatment. Forty-eight patients with ACS within the last 72 hours were randomized to received a single 20-minute session of either Reiki, classical music, or a control minimal distraction environment . All subjects underwent ambulatory ECG Holter monitoring. Emotional state was assessed by Likert scale. HRV was analyzed by spectral analysis via fast Fourier transformation during the baseline, intervention, and post-intervention periods and high-frequency power (lognormalized) compared via ANOVA with repeated measures. Adequate Holters were recorded in 12 control, 13 music, and 12 Reiki patients. High frequency (HF) component of HRV, an index of parasympathetic tone, increased significantly during Reiki (0.58±0.16) but not during music (-0.1±0.16) or control (0.06±0.16). RR interval increased significantly with Reiki and control, but not with music. Reiki significantly reduced reported anxiety and increased sense of relaxation compared to control (p=0.04), whereas music did not. In conclusion, post-MI recipients of light-touch from nurses trained in Reiki experienced increased vagal activity and decreased anxiety. Whether longer-term use of this therapy can improve outcomes requires further study

New visualization model for large scale biosignals analysis

Benefits of long-term monitoring have drawn considerable attention in healthcare. Since the acquired data provides an important source of information to clinicians and researchers, the choice for long-term monitoring studies has become frequent. However, long-term monitoring can result in massive datasets, which makes the analysis of the acquired biosignals a challenge. In this case, visualization, which is a key point in signal analysis, presents several limitations and the annotations handling in which some machine learning algorithms depend on, turn out to be a complex task. In order to overcome these problems a novel web-based application for biosignals visualization and annotation in a fast and user friendly way was developed. This was possible through the study and implementation of a visualization model. The main process of this model, the visualization process, comprised the constitution of the domain problem, the abstraction design, the development of a multilevel visualization and the study and choice of the visualization techniques that better communicate the information carried by the data. In a second process, the visual encoding variables were the study target. Finally, the improved interaction exploration techniques were implemented where the annotation handling stands out. Three case studies are presented and discussed and a usability study supports the reliability of the implemented work

Complexity index from a personalized wearable monitoring system for assessing remission in mental health

This study discusses a personalized wearable monitoring system, which provides information and communication technologies to patients with mental disorders and physicians managing such diseases. The system, hereinafter called the PSYCHE system, is mainly comprised of a comfortable t-shirt with embedded sensors, such as textile electrodes, to monitor electrocardiogram-heart rate variability (HRV) series, piezoresistive sensors for respiration activity, and triaxial accelerometers for activity recognition. Moreover, on the patient-side, the PSYCHE system uses a smartphone-based interactive platform for electronic mood agenda and clinical scale administration, whereas on the physician-side provides data visualization and support to clinical decision. The smartphone collects the physiological and behavioral data and sends the information out to a centralized server for further processing. In this study, we present experimental results gathered from ten bipolar patients, wearing the PSYCHE system, with severe symptoms who exhibited mood states among depression (DP), hypomania(HM), mixed state (MX), and euthymia (EU), i.e., the good affective balance. In analyzing more than 400 h of cardiovascular dynamics, we found that patients experiencing mood transitions from a pathological mood state (HM, DP, or MX - where depressive and hypomanic symptoms are simultaneously present) to EU can be characterized through a commonly used measure of entropy. In particular, the SampEn estimated on long-term HRV series increases according to the patients' clinical improvement. These results are in agreement with the current literature reporting on the complexity dynamics of physiological systems and provides a promising and viable support to clinical decision in order to improve the diagnosis and management of psychiatric disorders