Ambulatory Recording System Research Articles

Ambulatory ECG Recording System Based on ADS 1298 and STM32L431xx Microcontroller

Ambulatory ECG Recording System Based on ADS 1298 and STM32L431xx Microcontroller

Ambulatory gastric mucosal slow wave recording for chronic experimental studies.

Dysrhythmic bioelectric slow wave activity have been implicated in major functional motility disorders such as gastroparesis and chronic unexplained nausea and vomiting, but its correlation to symptoms is still unclear. For patients with severe gastroparesis, high-frequency gastric stimulation is offered as a therapy in some centers. Temporary gastric electrical stimulation has also been proposed an approach to screen patients who would benefit from the implantation of a permanent stimulator. In this study we introduced novel methods for recording slow wave activity from the gastric mucosa during the entire temporary stimulation phase of 5 days, in 3 patients. An ambulatory recording system was applied to record 3 channels of mucosal slow wave activity, as well as three axis accelerometer data to monitor when the patient was mobile. Techniques were developed to detect large movements and these time periods were excluded from analyses of mucosal slow waves. The frequency and amplitude of the slow waves was calculated in a 5 min segment, with 75% overlap, for the entire duration. In feasibility studies, the slow wave frequency and amplitude for the patients were 3.0±0.96 cpm and 1.43±1.75 mV. Large variations in slow wave amplitude were seen in comparsion to slow wave frequency, which were concordant with previous studies. The use of the ambulatory system will allow for investigation of pathophysiology, correlation of electrophysiology data to patient symptoms and to determine the effects of post-prandial and noctural slow wave patterns. We anticipate that future use of slow wave information alongside patient symptoms may allow improved selection of patients for stimulaton techniques.

Kaiser Permanente Northern California: Current Experiences With Internet, Mobile, And Video Technologies

The US health care system has been slow to adopt Internet, mobile, and video technologies, which have the capability to engage patients in their own care, increase patients' access to providers, and possibly improve the quality of care while reducing costs. Nevertheless, there are some pockets of progress, including Kaiser Permanente Northern California (KPNC). In 2008 KPNC implemented an inpatient and ambulatory care electronic health record system for its 3.4 million members and developed a suite of patient-friendly Internet, mobile, and video tools. KPNC has achieved many successes. For example, the number of virtual "visits" grew from 4.1 million in 2008 to an estimated 10.5 million in 2013. This article describes KPNC's experience with Internet, mobile, and video technologies and the obstacles faced by other health care providers interested in embracing them. The obstacles include the predominant fee-for-service payment model, which does not reimburse for virtual visits; the considerable investment needed to deploy these technologies; and physician buy-in.

Screening sleep disordered breathing in stroke unit.

In acute stroke, OSA has been found to impair rehabilitation and increase mortality but the effect of central apnea is more unclear. The aim of the present study was to evaluate the feasibility of using limited ambulatory recording system (sleep mattress to evaluate nocturnal breathing and EOG-electrodes for sleep staging) in sleep disordered breathing (SDB) diagnostics in mild acute cerebral ischemia patients and to discover the prevalence of various SDB-patterns among these patients. 42 patients with mild ischemic stroke or transient ischemic attack were studied. OSA was found in 22 patients (52.4%). Central apnea was found in two patients (4.8%) and sustained partial obstruction in only one patient (2.4%). Sleep staging with EOG-electrodes only yielded a similar outcome as scoring with standard rules. OSA was found to be common even after mild stroke. Its early diagnosis and treatment would be favourable in order to improve recovery and reduce mortality. Our results suggest that OSA can be assessed by a limited recording setting with EOG-electrodes, sleep mattress, and pulse oximetry.

Identification of Sleep Bruxism with an Ambulatory Wireless Recording System

To examine whether an ambulatory bruxism recording system, including a biologic monitor, that measures sleep variables and sympatho-vagal balance can specifically identify sleep bruxism (SB) at home. Twenty-six volunteers, including 16 SB subjects, were recruited. Each participant recorded his or her electromyogram (EMG), sympatho-vagal balance, and sound level for 3 consecutive nights using an audio-video recorder to identify SB. Data of sleep variables were compared among the 3 experimental nights. The episodes were classified into SB episodes with and without grinding and non-SB episodes. EMG patterns, amplitude, sympatho-vagal balance, and sound level of all episodes were analyzed so as to determine the appropriate thresholds to detect SB episodes and grinding sound. Then, all episodes without video-recording data were classified into SB and non-SB episodes by using the appropriate thresholds, and the sensitivity and specificity to detect SB episodes were calculated. With regard to sleep variables, there were no significant differences except for sleep latency between the first and second nights. The appropriate EMG pattern and thresholds of amplitude, sympatho-vagal balance, and sound level were phasic or mixed EMG pattern, 20% of maximum voluntary contraction, mean + 1 SD, and mean + 2 SDs, respectively. The sensitivity and specificity to detect SB episodes were 88.4% and 74.2%, respectively. The results suggest that this system enables the detection of SB episodes at home with considerably high accuracy and little interference with sleep.

Data reduction for cough studies using distribution of audio frequency content

BackgroundRecent studies suggest that objectively quantifying coughing in audio recordings offers a novel means to understand coughing and assess treatments. Currently, manual cough counting is the most accurate method for quantifying coughing. However, the demand of manually counting cough records is substantial, demonstrating a need to reduce record lengths prior to counting whilst preserving the coughs within them. This study tested the performance of an algorithm developed for this purpose.Methods20 subjects were recruited (5 healthy smokers and non-smokers, 5 chronic cough, 5 chronic obstructive pulmonary disease and 5 asthma), fitted with an ambulatory recording system and recorded for 24 hours. The recordings produced were divided into 15 min segments and counted. Periods of inactive audio in each segment were removed using the median frequency and power of the audio signal and the resulting files re-counted.ResultsThe median resultant segment length was 13.9 s (IQR 56.4 s) and median 24 hr recording length 62.4 min (IQR 100.4). A median of 0.0 coughs/h (IQR 0.0-0.2) were erroneously removed and the variability in the resultant cough counts was comparable to that between manual cough counts. The largest error was seen in asthmatic patients, but still only 1.0% coughs/h were missed.ConclusionsThese data show that a system which measures signal activity using the median audio frequency can substantially reduce record lengths without significantly compromising the coughs contained within them.

AUTOMATIC DETECTION OF EPILEPSY EEG USING NEURAL NETWORKS

The electroencephalogram (EEG) signal plays an important role in the diagnosis of epilepsy. The EEG recordings of the ambulatory recording systems generate very lengthy data and the detection of the epileptic activity requires a timeconsuming analysis of the entire length of the EEG data by an expert. The traditional methods of analysis being tedious, many automated diagnostic systems for epilepsy has emerged in recent years.This paper proposes a neural-network-based automated epileptic EEG detection system that uses approximate entropy (ApEn) as the input feature. ApEn is a statistical parameter that measures the predictability of the current amplitude values of a physiological signal based on its previous amplitude values. It is known that the value of the ApEn drops sharply during an epileptic seizure and this fact is used in the proposed system.Two different types of neural networks, namely, Elman and probabilistic neural networks are considered. ApEn is used for the first time in the proposed system for the detection of epilepsy using neural networks. It is shown that the overall accuracy values as high as 100% can be achieved by using the proposed system.

Epileptic Seizure Detection Using Discrete Wavelet Transform and Support Vector Machines

The electroencephalogram (EEG) signal plays an important role in the detection of epilepsy. The EEG recordings of the ambulatory recording systems generate very lengthy data and the detection of the epileptic activity requires a time- consuming analysis of the entire length of the EEG data by an expert. The aim of this work is to develop a new method for automatic detection of EEG patterns using Discrete wavelet Transform (DWT) and Support Vector Machines (SVM). Our method consists of EEG data collection, feature extraction and classification stages. DWT is used for feature extraction in the principle of time – frequency domain analysis. In classification stage we implement SVM to detect epileptic seizure. SVM provides binary classification between preictal/ictal and interictal states. The study is carried out on EEG recordings of two epileptic patients; two classification models are derived from each patient. The models are then tested on the same patient and the other patient, comparing the specificity, sensitivity and accuracy of each of the models. This model provides high sensitivity compare other detection method.

A new approach for epileptic seizure detection: sample entropy based feature extraction and extreme learning machine

The electroencephalogram (EEG) signal plays a key role in the diagnosis of epilepsy. Substantial data is generated by the EEG recordings of ambulatory recording systems, and detection of epileptic activity requires a time-consuming analysis of the complete length of the EEG time series data by a neurology expert. A variety of automatic epilepsy detection systems have been developed during the last ten years. In this paper, we investigate the potential of a recently-proposed statistical measure parameter regarded as Sample Entropy (SampEn), as a method of feature extraction to the task of classifying three different kinds of EEG signals (normal, interictal and ictal) and detecting epileptic seizures. It is known that the value of the SampEn falls suddenly during an epileptic seizure and this fact is utilized in the proposed diagnosis system. Two different kinds of classification models, back-propagation neural network (BPNN) and the recently-developed extreme learning machine (ELM) are tested in this study. Results show that the proposed automatic epilepsy detection system which uses sample entropy (SampEn) as the only input feature, together with extreme learning machine (ELM) classification model, not only achieves high classification accuracy (95.67%) but also very fast speed.

An ambulatory bruxism recording system with sleep-stage analyzing function

The aim of this study was to develop an ambulatory bruxism recording system capable of sleep-stage analysis. A portable EMG system was used to record masseter muscle activity. An EMG sensor was attached onto the masseter muscle belly at either side. EMG data were stored on a notebook type personal computer. A sound level meter was used to assess the sound level of bruxism. Sound level (dB) readings were taken every second and recorded on the same computer. A prototype of sleep sensor, a wristwatch-style biological signal sensor-recorder device, recorded and stored pulse wave, acceleration and temperature on a memory card. All stored data were transferred to a personal computer and analyzed. The whole system was transportable within a protective case and weighed approximately 5kg. Raw EMG signals were processed to derive integrated EMG data. TOSHIBA Sleep Analysis Program classified sleep-stages as awake, shallow sleep, deep sleep and REM based on the activity of the autonomic nervous system that was estimated from the fluctuations of pulse intervals. An EMG, sound level and sleep-stage analysis program was developed to analyze all data simultaneously. Using this program, the masseter muscle activity, sound level and sleep-stage could be quantified and correlated. We developed an ambulatory bruxism recording system that analyzes sleep-stage. We expect that this system will enable us to measure sleep bruxism activity in each sleep-stage on an electromyographical and auditory basis at the subject's home.

Usability of an Ambulatory Bruxism Recording System with Sleep-Stage Analyzing Function

We developed an ambulatory bruxism recording system that analyzes sleep stage. This system enables us to measure sleep bruxism activities in each sleep stage on an electromyographic and auditory basis at home. The purpose of this study was to examine the usability of this recording system. Six volunteers with self-awareness of sleep bruxism were recruited. They were instructed to operate the recording system by themselves and they recorded their bruxism activities at home. Afterward, they were asked to answer the questionnaire on the usability and the influence of the device on sleep. Data recordings were carried out in 3 to 5 nights per subject. All subjects could easily operate this system. Two subjects, however, failed to record in some nights. They reported problems of the sensors falling off from the skin. There was no complaint on disturbance of sleep due to the measurement. Although no interference to sleep was observed, our ambulatory recording system caused some technical difficulty in a few subjects. More detailed operation manual and simplified software seemed to be necessary.

Modificaciones en las perspectivas de los médicos sobre el registro médico electrónico: investigación cualitativa longitudinal

Objectives To explore physicians’ beliefs about a computerized ambulatory medical record system at different stages of its implementation. Methods We performed a longitudinal qualitative in-depth interview study (July 2001 to December 2003) in the Hospital Italiano, Buenos Aires, Argentina. Semi-structured interviews were conducted in 20 primary care cardiologists purposively selected before, during and afterthe system's implementation process (10 interviews perstage). The interviews were independently analyzed by 2 researchers, who jointly designed an agreed category list. Results Both before and during the first stage of the implementation process, the physicians expected that that the system would improve healthcare-related administration and increase accessibility to individual data. However, they did not foresee that the system's shared information could modify the clinical aspects of patient care. By the end of the implementation process, the physicians realized that the system provided them with a broader perspective on their patients, which in turn improved their own professional performance. Throughout the implementation, the physicians were against using the computer while the patient was present. This opposition prevented them from regarding the system as part of the medical consultation and from considering data from the system as direct patient-related signs. Conclusions The system's implementation modified the physicians’ views on computerized ambulatory medical records, as they eventually considered them as an ancillary tool to clinical activity. The value assigned to the system depends on its relevance within the institutional framework.

Approximate Entropy-Based Epileptic EEG Detection Using Artificial Neural Networks

The electroencephalogram (EEG) signal plays an important role in the diagnosis of epilepsy. The EEG recordings of the ambulatory recording systems generate very lengthy data and the detection of the epileptic activity requires a time-consuming analysis of the entire length of the EEG data by an expert. The traditional methods of analysis being tedious, many automated diagnostic systems for epilepsy have emerged in recent years. This paper proposes a neural-network-based automated epileptic EEG detection system that uses approximate entropy (ApEn) as the input feature. ApEn is a statistical parameter that measures the predictability of the current amplitude values of a physiological signal based on its previous amplitude values. It is known that the value of the ApEn drops sharply during an epileptic seizure and this fact is used in the proposed system. Two different types of neural networks, namely, Elman and probabilistic neural networks, are considered in this paper. ApEn is used for the first time in the proposed system for the detection of epilepsy using neural networks. It is shown that the overall accuracy values as high as 100% can be achieved by using the proposed system.

Gastric longitudinal shortening may occur during gastric phase III activities in man

The aim of this study was to determine whether the gastric longitudinal shortening occurs during the gastric phase III in man. Intragastric pH and gastroduodenal motility were simultaneously measured by means of a 24-h ambulatory recording system in 14 healthy volunteers. In nine subjects (group A), the catheter assembly was endoscopically clipped to the gastric mucosa with the middle transducer and the distal pH sensor in the antrum. In the remaining five subjects (group B), measurements were performed without securing the assembly. In 23 of the 25 gastrointestinal interdigestive migrating complexes in group A, the distal and middle transducers showed characteristic duodenal contractions (11-12 c.p.m.). Neutralization was noted at the distal pH sensor. Similar phenomena were observed during all 15 gastrointestinal interdigestive migrating complexes in group B. The catheter assemblies escaped into the duodenum despite the fact that they were secured to the stomach. This may be explained by gastric longitudinal shortening during gastric phase III activities. Studies of intragastric physiology with test catheters must take the effect of gastric longitudinal shortening during phase III into consideration.

Artificial Neural Network Based Epileptic Detection Using Time-Domain and Frequency-Domain Features

Electroencephalogram (EEG) signal plays an important role in the diagnosis of epilepsy. The long-term EEG recordings of an epileptic patient obtained from the ambulatory recording systems contain a large volume of EEG data. Detection of the epileptic activity requires a time consuming analysis of the entire length of the EEG data by an expert. The traditional methods of analysis being tedious, many automated diagnostic systems for epilepsy have emerged in recent years. This paper discusses an automated diagnostic method for epileptic detection using a special type of recurrent neural network known as Elman network. The experiments are carried out by using time-domain as well as frequency-domain features of the EEG signal. Experimental results show that Elman network yields epileptic detection accuracy rates as high as 99.6% with a single input feature which is better than the results obtained by using other types of neural networks with two and more input features.

Peripheral arterial tonometry, oximetry and actigraphy for ambulatory recording of sleep apnea

Autonomous nervous functions change with sleep stages and show characteristic changes associated with sleep disorders. Therefore, continuous monitoring of autonomous nervous functions during sleep can be used for diagnostic purposes. Recently, the peripheral arterial tonometry (PAT) has been introduced to determine peripheral arterial vascular tone on the finger being determined by sympathetic activity. We investigate a new ambulatory recording system which uses PAT, oximetry and actigraphy (Watch-PAT) in order to detect sleep apnea and arousal. The Watch-PAT is battery operated and attached to the wrist and has two finger sensors. Twenty-one patients with suspected sleep apnea were recorded with cardiorespiratory polysomnography and the new system in parallel. Seventeen recordings could be evaluated. The correlation for the apnea/hypopnea index derived from the sleep laboratory and the respiratory disturbance index derived from the Watch-PAT was r = 0.89 (p < 0.01) and between arousals and the respiratory disturbance index was r = 0.77 (p < 0.01). The correlation for the total sleep time compared between the two systems was r = 0.15 (n.s.). The Watch-PAT detects apneas and hypopneas with a reasonable reliability and it is very sensitive to arousals. The number of Watch-PAT events lies between the sum of apneas plus hypopneas and arousals. Arousals are not unique to apnea events and therefore the specifity of the Watch-PAT is limited. In conclusion, the Watch-PAT is well suited to perform therapy control studies in patients suffering from sleep apnea and being treated.

Development of a digital ambulatory recording system for electrodermal activity

A digital ambulatory recording system of electrodermal activity (D-EDA) that uses a multimedia card (MMC) as a compact data storage device was developed. Low power consumption allows 24 h simultaneous recording of skin conductance and/or skin potential. A reduction in weight and size of the system provided a more natural environment for ambulatory recording. Direct A/D conversion and storage to MMC remarkably simplified data processing. Improvement in time and amplitude resolution allowed observation during sleep of even fine fluctuations in skin potential and skin conductance, which had been buried in the rolling noise generated by our former analog cassette data recorder.

A neural-network-based detection of epilepsy

Diagnosis of epilepsy is primarily based on scalp-recorded electroencephalograms (EEG). Unfortunately the long-term recordings obtained from 'ambulatory recording systems' contain EEG data of up to one week duration, which has introduced new problems for clinical analysis. Traditional methods, where the entire EEG is reviewed by a trained professional, are very time-consuming when applied to recordings of this length. Therefore, several automated diagnostic aid approaches were proposed in recent years, in order to reduce expert effort in analyzing lengthy recordings. The most promising approaches to automated diagnosis are based on neural networks. This paper describes a method for automated detection of epileptic seizures from EEG signals using a multistage nonlinear pre-processing filter in combination with a diagnostic (LAMSTAR) Artificial Neural Network (ANN). Pre-processing via multistage nonlinear filtering, LAMSTAR input preparation, ANN training and system performance (1.6% miss rate, 97.2% overall accuracy when considering both false-alarms and 'misses') are discussed and are shown to compare favorably with earlier approaches presented in recent literature.

Effects of compression stockings on blood pressure and its orthostatic change in female subjects

We investigated in female non-hypertensive subjects whether compression stockings affected systolic (SBP) and diastolic blood pressures (DBP) and heart rate (HR) and whether they were effective in reducing changes in SBP induced by active standing. Using an automatic ambulatory recording system, these hemodynamic parameters were measured during the Schellong test in 82 females between 23 and 78 years of age (46±2 years; mean±S.E.). Measurement was carried out every 2 min according to the following protocol which was repeated with and without stockings: (1) while in a supine position for 10 min, (2) immediately on assuming a standing position and while maintaining this standing position for 15 min, (3) immediately on reassuming a supine position, and (4) while maintaining this supine position for 5 min. Wearing compression stockings significantly elevated SBP in the supine position of the hypotensive (SBP≤100 mmHg; n =24) but not normotensive groups (140 mmHg>SBP>100 mmHg; n =58). DBP and HR were not significantly altered in either group. Active standing resulted in either an increase or decrease in SBP of various degrees. Therefore, subjects were divided into the following three subgroups depending on the orthostatic change in SBP (∠SBP) calculated as the difference between SBP immediately after active standing and supine SBP:∠SBP≥20 mmHg ( n =10), 20>∠SBP≥0 mmHg ( n =57), and 0 mmHg>∠SBP ( n =15). In all the three groups, the absolute value of ∠SBP was significantly attenuated by the use of compression stockings. These effects of wearing stockings are not critically dependent on the age. We thus conclude that compression stockings are effective in elevating supine SBP of hypotensive subjects and in reducing the orthostatic response of SBP to active standing.

Computerized medical records and preventive health care: Success depends on many factors

objective: To study the effect of a computerized medical record and other practice factors on the delivery of preventive health care. design: Prospective, controlled trial. setting: University general internal medicine teaching clinic. participants: Forty-five internal medicine residents and their 4 supervising attending physicians. intervention: The study group used a computerized ambulatory medical record system that included health care maintenance reminders. The control group used a conventional paper record with a health care maintenance flow sheet. measurements and main results: The computer reminders significantly increased health care maintenance recommendations made to patients for proctosigmoidoscopy, tetanus vaccination, influenza vaccination, and pneumococcal vaccination, but not for fecal occult blood testing, mammography, Pap smears, or serum thyroxme screening in the elderly. First-year residents were nearly twice as successful as third-year residents in overall health care maintenance. Success scores varied markedly depending on which attending physician was supervising the residents. We found a strong interaction among group assignment, supervising attending, and level of training such that the reminders doubled success scores among firstyear residente supervised by two of the attending physicians but had little effect on other subgroups. The time of year and the format of the reminder also had important effects for some of the maneuvers. conclusions: Although computerized medical records markedly unproved the performance of prevention maneuvers by committed physicians, many physicians using computer systems failed to make use of the resource. The reasons for this were complex. Future work in this area should carefully control for personal behaviors and focus upon administrative changes that more effectively implement these potentially powerful tools.