Antti Sarela

Automatic Detection of Respiration Rate From Ambulatory Single-Lead ECG

Ambulatory electrocardiography is increasingly being used in clinical practice to detect abnormal electrical behavior of the heart during ordinary daily activities. The utility of this monitoring can be improved by deriving respiration, which previously has been based on overnight apnea studies where patients are stationary, or the use of multilead ECG systems for stress testing. We compared six respiratory measures derived from a single-lead portable ECG monitor with simultaneously measured respiration air flow obtained from an ambulatory nasal cannula respiratory monitor. Ten controlled 1-h recordings were performed covering activities of daily living (lying, sitting, standing, walking, jogging, running, and stair climbing) and six overnight studies. The best method was an average of a 0.2-0.8 Hz bandpass filter and RR technique based on lengthening and shortening of the RR interval. Mean error rates with the reference gold standard were plusmn4 breaths per minute (bpm) (all activities), plusmn2 bpm (lying and sitting), and plusmn1 breath per minute (overnight studies). Statistically similar results were obtained using heart rate information alone (RR technique) compared to the best technique derived from the full ECG waveform that simplifies data collection procedures. The study shows that respiration can be derived under dynamic activities from a single-lead ECG without significant differences from traditional methods.

Empowering Citizens for Well-being and Chronic Disease Management With Wellness Diary

Chronic conditions closely related to lifestyles are the major cause of disability and death in the developed world. Behavior change is the key to managing well-being and preventing and managing chronic diseases. Wellness diary (WD) is a mobile application designed to support citizens in learning about their behavior, and both making and maintaining behavior changes. WD has been found acceptable, useful, and suitable for long-term use as a part of an intervention. When used independently, however, it does not seem to have enough engaging and motivating features to support adoption and long-term commitment. The main improvement needs identified based on a review of WD-related studies were: personalization of the application to individual needs, increasing motivation during early use, maintaining motivation, and aiding in relapse recovery in long-term use. We present concepts to improve the personalization of WD as well as improvements to the feedback and interpretation of the self-observation data. We also present usage models on how this type of mobile application could be utilized.

Circadian activity rhythm in demented and non-demented nursing-home residents measured by telemetric actigraphy

There is a need to develop unobtrusive methods for long‐term monitoring of sleep/wake and circadian activity patterns in the elderly both in nursing homes and at home settings as sleep is important for health and well‐being. The IST Vivago® WristCare is an active social alarm system, which provides continuous telemetric monitoring of the users activity. We examined how the activity signal measured by IST Vivago differed between demented and non‐demented subjects living in a nursing home, and how it correlated with the subjective assessment of sleep quality and daytime alertness. The activity signal data together with subjective assessments of sleep quality and daytime vigilance were collected from 42 volunteers (aged 56–97 years; 23 demented and 19 non‐demented) for at least 10 days. The demented subjects had lower daytime activity and higher nocturnal activity than the non‐demented subjects. Correlations between the activity parameters and self‐assessments were weak but statistically significant. We also found correlation between functional ability and diurnal activity. The results are in line with previous studies with demented and non‐demented elderly subjects and suggest that the IST Vivago system provides a valid instrument for unobtrusive continuous long‐term monitoring of the circadian rhythm and sleep/wake patterns in the elderly.

Wavelet based approach for posture transition estimation using a waist worn accelerometer

The ability to rise from a chair is considered to be important to achieve functional independence and quality of life. This sit-to-stand task is also a good indicator to assess condition of patients with chronic diseases. We developed a wavelet based algorithm for detecting and calculating the durations of sit-to-stand and stand-to-sit transitions from the signal vector magnitude of the measured acceleration signal. The algorithm was tested on waist worn accelerometer data collected from young subjects as well as geriatric patients. The test demonstrates that both transitions can be detected by using wavelet transformation applied to signal magnitude vector. Wavelet analysis produces an estimate of the transition pattern that can be used to calculate the transition duration that further gives clinically significant information on the patients condition. The method can be applied in a real life ambulatory monitoring system for assessing the condition of a patient living at home.

Detecting walking activity in cardiac rehabilitation by using accelerometer

This study is part of the ongoing care assessment platform project, which involves monitoring vital signs and daily activity profile information of chronic disease patients undergoing cardiac rehabilitation. In this study, we have focussed on detecting walking activity from a cardiac rehab session which includes many other high intensity activities such as biking and rowing, using waist worn accelerometers. Walking is an important measure useful to assess the mobility of elderly people. Various methods have been proposed in the literature to identify walking from waist worn accelerometer signals based on wavelet, frequency and computational intelligence methods. Wavelet based approach, due to its feasibility to be implemented in real time with low computational complexity, good accuracies and also the ability to provide good time frequency resolution, has been the most desirable approach. In this study, we have evaluated and compared six wavelet decomposition based measures to detect walk from other high intensity activities. The different measures were derived using anterior-posterior, vertical, medio-lateral and signal vector magnitude (SVM) acceleration signals. The results show that all these measures can discriminate walking from other high intensity activities and the SVM based measure was the most efficient (89.14% sensitivity and 89.97 % specificity).

A home-based care model for outpatient cardiac rehabilitation based on mobile technologies

Cardiac rehabilitation programs offer effective means to prevent recurrence of a cardiac event. However, poor uptake of current programs have been reported globally. Home based model is considered as an alternative to avoid barriers related to hospital based programs. The challenge in a home based program is to re-establish the link between the patient and clinician regardless of their location. Mobile phones and internet technologies can potentially be used to enable a comprehensive home based care model. We developed tools and integrated model for home based cardiac rehabilitation based on a mobile phone with integrated accelerometer to measure physical exercise and diary to collect information on physiological risk factors and other health information. Video and teleconferencing are used for mentoring sessions and educational multimedia content is transferred via messaging systems to the patients phone to be viewed on demand. The model can be extended to management of other chronic conditions.

Mobile tools for home-based cardiac rehabilitation based on heart rate and movement activity analysis

Cardiac rehabilitation programs are traditionally carried out in hospitals and health centers under the direct personal supervision of mentoring clinicians. Patient barriers, such as time constraints and distance from treatment centers, have lead to poor uptake of programs among eligible patients. To overcome these barriers, home-based care models have been proposed as a viable alternative to hospital-based cardiac rehabilitation programs. We have developed a measurement system and software tools on a mobile phone platform enabling patients to participate in a home-based cardiac rehabilitation exercise program. A mobile application, TuneWalk, gives guidance to the patients during home exercises using heart rate and physical activity analysis and also stores long-term information about their progress during the weeks of the rehabilitation program. The measured data are also sent to a server for remote exercise performance analysis and consultation by the patient’s personal mentor.

Service and business model for technology enabled and home-based cardiac rehabilitation programs

Cardiac rehabilitation programs are comprehensive life-style programs aimed at preventing recurrence of a cardiac event. However, the current programs have globally significantly low levels of uptake. Home-based model can be a viable alternative to hospital-based programs. We developed and analysed a service and business model for home based cardiac rehabilitation based on personal mentoring using mobile phones and web services. We analysed the different organizational and economical aspects of setting up and running the home based program and propose a potential business model for a sustainable and viable service. The model can be extended to management of other chronic conditions to enable transition from hospital and care centre based treatments to sustainable home-based care.

Care assessment platform: An ICT-enabled home care model for secondary prevention of cardiovascular diseases

Background: Cardiovascular disease (CVD) is the leading chronic diseases affecting developed countries. Traditional approach to secondary prevention of CVD through hospital-based cardiac rehabilitation (CR) is hampered by the lack of uptake and adherence.

A home-based care model of cardiac rehabilitation using digital technology

Cardiovascular disease (CVD) is the number one cause of death globally. There already exists a structured guideline to cardiac rehabilitation for CVD patients as a means of preventing recurrence(s) of any cardiac events and return to an active, healthy and satisfying lifestyle. Despite the availability of cardiac rehabilitation programs, utilisation among eligible patients has been less than 20%. The barriers to this underutilisation have been factors relating to patients, services, and professionals. An alternative approach is a home-based cardiac rehabilitation has shown some improvements in the patients’ uptake of these services. Recent developments in physiological monitoring, information processing, and communication technologies have shown potential to enable a home-based cardiac rehabilitation program for better uptake and adherence and coordination between a team of multidisciplinary carers. One approach has been to use communication technologies such as mobile phone platform to help improve the carers’ ability to give multimodal feedback to the patients regularly and enable the use of other multimedia formats.