Umit Deniz Ulusar

Adaptive rule based fetal QRS complex detection using hilbert transform

In this paper we introduce an adaptive rule based QRS detection algorithm using the Hilbert transform (adHQRS) for fetal magnetocardiography processing. Hilbert transform is used to combine multiple channel measurements and the adaptive rule based decision process is used to eliminate spurious beats. The algorithm has been tested with a large number of datasets and promising results were obtained.

A Novel Approach to Track Fetal Movement Using Multi-sensor Magnetocardiographic Recordings

Changes in fetal magnetocardiographic (fMCG) signals are indicators for fetal body movement. We propose a novel approach to reliably extract fetal body movements based on the field strength of the fMCG signal independent of its frequency. After attenuating the maternal MCG, we use a Hilbert transform approach to identify the R-wave. At each R-wave, we compute the center-of-gravity (cog) of the coordinate positions of MCG sensors, each weighted by the magnitude of the R-wave amplitude recorded at the corresponding sensor. We then define actogram as the distance between the cog computed at each R-wave and the average of the cog from all the R-waves in a 3-min duration. By applying a linear de-trending approach to the actogram we identify the fetal body movement and compare this with the synchronous occurrence of the acceleration in the fetal heart rate. Finally, we apply this approach to the fMCG recorded simultaneously with ultrasound from a single subject and show its improved performance over the QRS-amplitude based approach in the visually verified movements. This technique could be applied to transform the detection of fetal body movement into an objective measure of fetal health and enhance the predictive value of prevalent clinical testing for fetal wellbeing.

Recovery of gastrointestinal tract motility detection using Naive Bayesian and minimum statistics

Loss of gastrointestinal motility is a significant medical setback for patients who experience abdominal surgery and contributes to the most common reason for prolonged hospital stays. Recent clinical studies suggest that initiating feeding early after abdominal surgery is beneficial. Early feeding is possible when the patients demonstrate bowel motility in the form of bowel sounds (BS). This work provides a data collection, processing and analysis methodology for detection of recovery of gastrointestinal track motility by observing BSs in auscultation recordings. The approach is suitable for real-time long-term continuous monitoring in clinical environments. The system was developed using a Naive Bayesian algorithm for pattern classification, and Minimum Statistics and spectral subtraction for noise attenuation. The solution was tested on 59h of recordings and 94.15% recognition accuracy was observed.

Real-time monitoring for recovery of gastrointestinal tract motility detection after abdominal surgery

Loss of gastrointestinal motility occurs for patients who experience abdominal surgery and in order to avoid postoperative nausea and vomiting a period of fasting is commonly practiced. This study presents a system which acquires bowel sound signals by means of a devised stethoscope, performs real-time signal processing and notifies clinicians if bowel activity is present. The idea behind this practice is to provide enough time for intestinal motility to return to normal and benefit from early feeding in order to shorten hospital stay. Research findings indicate that a detection algorithm using statistical approach is suitable for real time monitoring in noisy clinical environments.

Bio-magnetic signatures of fetal breathing movement.

The purpose of fetal magnetoencephalography (fMEG) is to record and analyze fetal brain activity. Unavoidably, these recordings consist of a complex mixture of bio-magnetic signals from both mother and fetus. The acquired data include biological signals that are related to maternal and fetal heart function as well as fetal gross body and breathing movements. Since fetal breathing generates a significant source of bio-magnetic interference during these recordings, the goal of this study was to identify and quantify the signatures pertaining to fetal breathing movements (FBM). The fMEG signals were captured using superconducting quantum interference devices (SQUIDs) The existence of FBM was verified and recorded concurrently by an ultrasound-based video technique. This simultaneous recording is challenging since SQUIDs are extremely sensitive to magnetic signals and highly susceptible to interference from electronic equipment. For each recording, an ultrasound-FBM (UFBM) signal was extracted by tracing the displacement of the boundary defined by the fetal thorax frame by frame. The start of each FBM was identified by using the peak points of the UFBM signal. The bio-magnetic signals associated with FBM were obtained by averaging the bio-magnetic signals time locked to the FBMs. The results showed the existence of a distinctive sinusoidal signal pattern of FBM in fMEG data.

Wireless Communication Aspects in the Internet of Things: An Overview

Recent advances in technology propelled the development of resource constrained tiny devices and the concept of Internet of Things (IoT). Potential applications spanning various fields of science from environmental to medical have been emerged. Different architectures, routing protocols, performance issues and goals have been suggested. In this work, we review fundamental concepts, recent developments and critical design factors under IoT-specific constraints and objectives such as energy efficiency and environment protection.

An overview of Internet of things and wireless communications

Innovations in technology that have enabled efficient wireless tiny devices propelled the concept of Internet of Things. It is expected that mobile data traffic will experience 8-fold growth between 2015 and 2020 and the number of mobile connected devices will reach 11.6 billion by 2020. Main factors of this exponential growth and wide acceptance are the integration of several technologies and communications solutions such as wired and wireless sensor and actuator networks, next generation communication protocols, identification technologies, and artificial intelligence for smart objects. In this work, we explore the role of the IoT in various fields, consider technological aspects, and examine the challenges and opportunities the IoT offers.

Bioacoustic sensor system for automatic detection of bowel sounds

This study presents a bioacoustic sensor system developed for early detection of the recovery of bowel activity after abdominal surgery and to perform analysis on bowel sounds. Different than other studies, in order to be able to attenuate noise, two capacitive microphones oriented in opposite directions are used. Bowel sounds are typically observed at a frequency between 100 Hz and 1500 Hz and amplitude between 0 and 20mV. The signal strength is boosted 48 times with an amplifying circuit. The second microphone is used to observe environmental noise such as examination room noise. Both signals are digitized using an ADC (NI DAQ Data Acquisition USB 6009). Finally, we developed a software that can extract spectral properties of the signal and present the results in real time.

A computer-aided approach to detect the fetal behavioral states using multi-sensor Magnetocardiographic recordings

We propose a novel computational approach to automatically identify the fetal heart rate patterns (fHRPs), which are reflective of sleep/awake states. By combining these patterns with presence or absence of movements, a fetal behavioral state (fBS) was determined. The expert scores were used as the gold standard and objective thresholds for the detection procedure were obtained using Receiver Operating Characteristics (ROC) analysis. To assess the performance, intraclass correlation was computed between the proposed approach and the mutually agreed expert scores. The detected fHRPs were then associated to their corresponding fBS based on the fetal movement obtained from fetal magnetocardiogaphic (fMCG) signals. This approach may aid clinicians in objectively assessing the fBS and monitoring fetal wellbeing.

Wireless bioacoustic sensor system for automatic detection of bowel sounds

Due to anesthesia loss of gastrointestinal motility is a common situation for patients who underwent abdominal surgery. The aim of this study is to present the bioacoustic sensor system developed for real time detection of recovery of gastrointestinal tract motility by observing bowel sounds and for bowel sound signal analysis. Different than other studies, in this study, in order to be able to attenuate environmental noise, bowel sounds were observed using two microphones. Bowel sounds were observed between 100 Hz and 1 kHz with maximum amplitude of 20mV. The signal was amplified 121 times and final signal amplitude was between 0-2, 4V. Similarly, environmental noise observed by the second microphone was amplified. Both of the signals were digitized using 12 bit analog digital converter of the ZigBee module (JN5139-Z01) and were transferred with wireless connection. With the developed software, spectral and temporal properties were obtained and presented in real-time. Finally, a stethoscope shaped box was designed for easy use of the sensor system.