Respiratory Signal Acquisition Research Articles

Validation of four-dimensional computed tomography without external reference respiratory signals for radiation treatment planning of lung tumors

Deviceless four-dimensional (4D) computed tomography (CT) allows the acquisition of respiratory signals from six features without requiring an external device for cine CT processing. This method has been recently introduced in radiation treatment planning of lung tumors. To validate deviceless 4D CT, it must be compared with conventional 4D CT, which requires an external monitoring device. We compared the two methods using a multicell 4D phantom that simulates patient’s movement during respiration regarding the target volume (TV), target position (TP), and internal TV for lung tumor radiation therapy. We retrospectively obtained images of 10 patients who underwent radiation treatment planning of lung tumors and compared the two methods, as in the phantom study. For the phantom study, the mean TV, root mean square errors of the TP, and mean internal TV differences between the two methods ranged from −4.5% to 1.2%, 0.7 to 2.6 mm, and −1.1% to 3.4%, respectively. The corresponding results of the clinical study ranged from −1.5% to 14.9%, 0.1 to 5.9 mm, and −9.7% to 10.1%, respectively. The results of deviceless 4D CT for the clinical study were consistent with those of conventional 4D CT, except for target movements with high excursions. Therefore, deviceless 4D CT can be an alternative to conventional 4D CT for radiation treatment planning of lung tumors.

Development of Respiratory Signal Monitoring System Based on Photoplethysmography

Breathing is of great significance in the monitoring of patients with obstructive sleep apnea hypopnea syndrome, perioperative monitoring and intensive care. In this study, a respiratory monitoring and verification system based on optical capacitance product pulse wave (PPG) is designed, which can synchronously collect human PPG signals. Through algorithm processing, the characteristic parameters of PPG signal are calculated, and the respiratory signal and respiratory frequency can be extracted in real time. In order to verify the accuracy of extracting respiratory signal and respiratory rate by the algorithm, the system adds the nasal airflow respiratory signal acquisition module to synchronously collect the nasal airflow respiratory signal as the standard signal for comparison and verification. Finally, the root mean square error between the respiratory rate extracted by the algorithm from the pulse wave and the standard respiratory rate is only 1.05 times/min.

Design of Low Power Multi-parameter Monitoring System Based on Bluetooth

This paper designs a bluetooth-based low-power multi-parameter monitoring system. The system is mainly composed of ECG signal acquisition, respiratory signal acquisition, body temperature acquisition, bluetooth 4.0 transmission module and Android mobile phone APP display. The system collects the corresponding physiological signals through various collection parts, and can realize the monitoring of three physiological signals of electrocardiogram, respiration and body temperature. The Android mobile APP can display ECG, respiratory waveform and temperature data in real time. The system is small in size and low in power consumption, and has a good application prospect in portable and wearable medical applications.

Design and Application of Human Movement Respiratory and ECG Signal Acquisition System

To realize the design of mobile phone human movement breathing and electrocardiograph (ECG) signal acquisition system based on Bluetooth transmission, the principle of the generation and detection of ECG and respiratory signal and the guide system of signal acquisition are analyzed. Additionally, the hardware of the system is designed, including the hardware of the signal acquisition system, the design of ADS1292R ECG and respiratory signal acquisition module, the design of the main control chip and the design of the Bluetooth module. Then, the digital filtering processing of the ECG and respiratory signals is completed, including the baseline drift filtering and the suppression of the power frequency interference. The results show that the monitoring system runs well and it can effectively collect ECG and respiratory signals, calculate heart rate and respiratory frequency in real time, and display ECG waveform in real time. To sum up, the monitoring system is of great significance for real-time monitoring of the patient's condition.

Study on the usage feasibility of continuous-wave radar for emotion recognition

Non-contact vital signs monitoring has a wide range of applications, such as in safe drive and in health care. In mental health care, the use of non-invasive signs holds a great potential, as it would likely enhance the patient's adherence to the use of objective measures to assess their emotional experiences, hence allowing for more individualized and efficient diagnoses and treatment. In order to evaluate the possibility of emotion recognition using a non-contact system for vital signs monitoring, we herein present a continuous wave radar based on the respiratory signal acquisition. An experimental set up was designed to acquire the respiratory signal while participants were watching videos that elicited different emotions (fear, happiness and a neutral condition). Signal was registered using a radar-based system and a standard certified equipment. The experiment was conducted to validate the system at two levels: the signal acquisition and the emotion recognition levels. Vital sign was analysed and the three emotions were identified using different classification algorithms. Furthermore, the classifier performance was compared, having in mind the signal acquired by both systems. Three different classification algorithms were used: the support-vector machine, K-nearest neighbour and the Random Forest. The achieved accuracy rates, for the three-emotion classification, were within 60% and 70%, which indicates that it is indeed possible to evaluate the emotional state of an individual using vital signs detected remotely.

A Smart Phone Based Handheld Wireless Spirometer with Functions and Precision Comparable to Laboratory Spirometers.

We report a smart phone based handheld wireless spirometer which uses a Lilly type sensing flowhead for respiratory signal acquisition and transmits the data to smartphone or other mobile terminals with Bluetooth signal transmission for data processing and result display. The developed spirometer was demonstrated to be able to detect flow rates ranging from 0–15 L/s with an accuracy of 4 mL/s, and can perform tests of flow volume (FV), forced vital capacity (FVC), forced expiratory volume in 1 s (FEV1), peak expiratory flow (PEF), etc. By having the functions and precision comparable to laboratory spirometers, it satisfies the American Thoracic Society and European Respiratory Society (ATS/ERS) proposed performance requirements for spirometer. At the same time, it is low cost, light and handy, low power consumption battery-powered. The test of 12 cases of subjects using the developed spirometer also indicated that it was easy to use for both providers and patients, and suitable for the Point of Care Test (POCT) of chronic obstructive pulmonary disease (COPD) and asthma at general-practice settings and homes.

Automatic respiratory phase detection for functional electrical stimulation synchronization

Abstract Introduction People with cervical or high thoracic spinal cord injury usually have respiratory muscle weakness. When transcutaneous functional electrical stimulation (TFES) synchronized with the patient’s natural breathing is applied to respiratory muscles, their strength and resistance are increased. In this work, we propose a novel method to perform an automatic synchronization, composed of a signal acquisition system and an algorithm that recognizes both respiratory cycle phases during quiet breathing. Methods The respiratory signal acquisition unit consists of a load cell attached to an elastic belt. The algorithm is based on statistical evaluation and linear approximation for detecting the beginning of both inhalation and exhalation phases. Ten volunteers remained steady, breathing quietly for one minute for signal acquisition. Results The system’s automatic detection of inspiratory events reached 87.5% of true positives, 6.7% of false negatives and 5.8% of false positives. Both hit and error ratios obtained in the detection of expiratory events reached 94.3% true positives, 4.9% false positives and 0.8% false negatives. Conclusion The developed algorithm can identify the respiratory phases properly and it can be used in future synchronized TFES applications whether the patient remains in a quasi-static position during treatment.

Low-power system for the acquisition of the respiratory signal of neonates using diaphragmatic electromyography.

IntroductionAn apnea episode is defined as the cessation of breathing for ≥15 seconds or as any suspension of breathing accompanied by hypoxia and bradycardia. Obtaining information about the respiratory system in a neonate can be accomplished using electromyography signals from the diaphragm muscle.ObjectiveThe purpose of this paper is to illustrate a method by which the respiratory and electrocardiographic signals from neonates can be obtained using diaphragmatic electromyography.Materials and methodsThe system was developed using single-supply, micropower components, which deliver a low-power consumption system appropriate for the development of portable devices. The stages of the system were tested in both adult and neonate patients.ResultsThe system delivers signals as those expected in both patients and allows the acquisition of respiratory signals directly from the diaphragmatic electromyography.ConclusionThis low-power system may present a good alternative for monitoring the cardiac and respiratory activity in newborn babies, both in the hospital and at home.SignificanceThe system delivers good signals but needs to be validated for its use in neonates. It is being used in the Neonatal Intensive Care Unit of the Hospital General de Medellín Luz Castro de Gutiérrez.

Bluetooth-Based Wearable Human Respiratory Parameter Monitoring System Design

Designed a Bluetooth-based wearable SAHS (sleep apnea hypopnea syndrome) monitoring device, the device can be real-time monitoring in the SAHS patient's respiratory state. Meanwhile, wearable technology achieves basic respiratory signal acquisition for low physiological and psychological load; Bluetooth achieves short-distance wireless transmission of respiration data and convenient communication with the handheld terminal such as PDA or smart phone, to ensure the continuous real-time monitoring of the patient.

A new fast and fully automated software based algorithm for extracting respiratory signal from raw PET data and its comparison to other methods

Respiratory gating in PET is an approach used to minimize the negative effects of respiratory motion on spatial resolution. It is based on an initial determination of a patient's respiratory movements during a scan, typically using hardware based systems. In recent years, several fully automated databased algorithms have been presented for extracting a respiratory signal directly from PET data, providing a very practical strategy for implementing gating in the clinic. In this work, a new method is presented for extracting a respiratory signal from raw PET sinogram data and compared to previously presented automated techniques. The acquisition of respiratory signal from PET data in the newly proposed method is based on rebinning the sinogram data into smaller data structures and then analyzing the time activity behavior in the elements of these structures. From this analysis, a 1D respiratory trace is produced, analogous to a hardware derived respiratory trace. To assess the accuracy of this fully automated method, respiratory signal was extracted from a collection of 22 clinical FDG-PET scans using this method, and compared to signal derived from several other software based methods as well as a signal derived from a hardware system. The method presented required approximately 9 min of processing time for each 10 min scan (using a single 2.67 GHz processor), which in theory can be accomplished while the scan is being acquired and therefore allowing a real-time respiratory signal acquisition. Using the mean correlation between the software based and hardware based respiratory traces, the optimal parameters were determined for the presented algorithm. The mean/median/range of correlations for the set of scans when using the optimal parameters was found to be 0.58/0.68/0.07-0.86. The speed of this method was within the range of real-time while the accuracy surpassed the most accurate of the previously presented algorithms. PET data inherently contains information about patient motion; information that is not currently being utilized. We have shown that a respiratory signal can be extracted from raw PET data in potentially real-time and in a fully automated manner. This signal correlates well with hardware based signal for a large percentage of scans, and avoids the efforts and complications associated with hardware. The proposed method to extract a respiratory signal can be implemented on existing scanners and, if properly integrated, can be applied without changes to routine clinical procedures.

Poster — Thur Eve — 40: Evaluation of Respiratory‐Induced Motion

Purpose: Collect statistics on respiratory induces internal motion to improve population based margin definition of target volumes and organs at risks and to determine the feasibility of screening patients that would most benefits from a 4DCT exam. Methods: Motion of relevant internal and external region of interest was evaluated on 4DCT datasets of 16 patients in an open study. Patients were selected based on tumour location. For a given 4DCT dataset, motion was characterized for each of 10 phases of the breathing cycle. Trace differences between two methods of respiratory signal acquisition were also evaluated. Results: Motion in the superior‐inferior (SI) direction, unlike other directions, showed a uniform motion trend between different structures. External markers have an opposed phase motion compared to internal structures. Diaphragm usually showed the largest averaged magnitude of motion (1.55±0.50 cm) with a wide range of amplitude. As can be expected, gastro‐intestinal tumors (0.88±0.27 cm) are in average less mobile than lung tumors (1.18±0.90 cm). Respiratory signal acquisition systems used for this study produced very similar traces regarding baseline variation and peak shape. Conclusion: Population‐based specific organs motion will be used for personalized treatment planning (e.g. delineation of margins, contours). Most structures have a similar shaped motion (i.e. no phase mismatch) and amplitude in the SI direction, except for external markers. Abdomen AP motion is a promising external indicator of internal motion magnitude.

A four-dimensional controller for DMLC-based tumor tracking

The development of four-dimensional (4D) radiotherapy for dynamic multileaf collimator (DMLC)-based tumor tracking requires the development of new hardware and software for controlling the radiation delivery, termed a four-dimensional controller (4DC). The four-dimensional controller integrates the information from the treatment plan and the respiration signal and computes multileaf collimator positions which are then sent to the DMLC to facilitate tumor tracking. The purpose of this study was to (a) develop a prototype 4DC and (b) determine the efficacy of the 4DC for 4-dimensional radiotherapy. In this work, three key components of 4DC were studied: (1) the effect of respiratory coaching on the reproducibility of respiratory patterns, (2) the mechanical ability of the DMLC to perform tumor tracking and (3) the ability to predict respiratory patterns to account for the delay between respiratory signal acquisition and DMLC. (1) Respiratory coaching: Respiration reproducibility is an important component, not just of 4D radiation delivery, but also 4DCT acquisition and 4D treatment planning. To investigate if coaching methods can improve respiration reproducibility, 300 four-minute respiratory traces were obtained from 23 lung cancer patients enrolled in an IRB-approved study under the conditions of free breathing, audio coaching, and audio-visual coaching. Respiration reproducibility was quantified by cycle-to-cycle position, displacement and breathing period variations. (2) DMLC capability: To investigate the mechanical capabilities of the DMLC to perform tumor tracking for respiratory motion, the maximum leaf velocity, acceleration and deceleration were measured for three different MLCs at different time periods and under various gravitational and friction environments. The measured values were input into equations of motion to determine the DMLC response time for positional changes under various initial and final conditions. The DMLC response was compared with fluoroscopy-measured diaphragm motion from 60 data-sets from five lung cancer patients. (3) Respiration prediction: To account for the system response time between respiration signal acquisition and execution of the MLC motion, a linear adaptive prediction filter was employed. This filter was implemented and the prediction accuracy tested on sixty fluoroscopy datasets from lung cancer patients in which the diaphragm motion was tracked for system response times of 0.2, 0.4 and 0.6 seconds. (1) Respiratory coaching: Respiratory reproducibility is significantly improved with audio-visual coaching. The variation in breathing rate and position and displacement was less for audio-visual coaching than free breathing and audio coaching. (2) DMLC capability: Significant differences were observed between inner (0.5 cm thick) and outer (1.0 cm thick) leaves. The DMLC maximum leaf velocities are 3.9 ± 0.5 cm/s and 3.3 ± 0.1 cm/s respectively and acceleration 37 ± 8 cm/s2 and 37 ± 10 cm/s2. Based on these measurements, the mechanical MLC delay time is around 0.1 seconds leading to an overall system response time of approximately 0.4 seconds. Applying equations of motion derived from these measurements to fluoroscopy-measured internal motion indicates that DMLC tracking will be over 95% efficient, however a beam-hold is required to account for irregular respiration such as coughing. (3) Respiration prediction: The ability to predict respiratory motion as the system response time increases. However, for a response time of 0.4 seconds (the estimated value for 4D radiotherapy utilizing current technology) the position prediction error is less than 2 mm (1.s.d.). A prototype version of a four-dimensional controller for DMLC-based tumor tracking has been developed. Based on patient respiratory and fluoroscopy analysis, and DMLC mechanical measurements and analysis, the existing linear accelerator technology is capable of 4D radiation delivery.