A new method for feedback on the quality of chest compressions during cardiopulmonary resuscitation

An evolutionary approach to continuously estimate CPR quality parameters from a wrist-worn inertial sensor

Cardiopulmonary resuscitation (CPR) is one of the most critical emergency interventions for sudden cardiac arrest. In this paper, a robust sinusoidal model-fitting method based on a Evolution Strategy inspired algorithm for CPR quality parameters – naming chest compression frequency and depth – as measured by an inertial measurement unit (IMU) attached to the wrist is presented. The proposed approach will allow bystanders to improve CPR as part of a continuous closed-loop support system once integrated into a smartphone or smartwatch application. By evaluating the model’s precision with data recorded by a training mannequin as reference standard, a variance for the compression frequency of $$\pm 2.22$$ ± 2.22 compressions per minute (cpm) has been found for the IMU attached to the wrist. It was found that this previously unconsidered position and thus, the use of smartwatches is a suitable alternative to the typical placement of phones in hand for CPR training.

A simple method for removing initial irregularity of an electrocardiogram during a transient state of a power supply in a defibrillator

BACKGROUND:

The defibrillator is a device that instantaneously discharges the high energy stored in the capacitor to the human body to help revitalize the heart. The circuit for charging the capacitor uses the same power source as the biosignal measurement unit. Therefore, variation in main power supply voltage, ground noise, and electromagnetic interference from the charging circuit can induce distortion into the biosignal at the initial stage of charging.

OBJECTIVE:

In this study, a simple method is proposed for removing the initial irregularity of an electrocardiogram due to the transient state of a power supply.

METHODS:

To evaluate the method, a 1-channel electrocardiogram measurement unit and peripheral units were separated from the main control module using galvanic isolation. An isolated push-pull converter was designed to power the secondary side. The method was tested under steady-state and transient conditions.

RESULTS:

The obtained results proved that biosignal distortion can be significantly reduced.

CONCLUSION:

This method could be another simple implementation approach for solving signal distortions due to the transient status of power supplies used in medical devices.

A novel depth estimation algorithm of chest compression for feedback of high-quality cardiopulmonary resuscitation based on a smartwatch

INTRODUCTION

High-quality cardiopulmonary resuscitation (CPR) is a key factor affecting cardiac arrest survival. Accurate monitoring and real-time feedback are emphasized to improve CPR quality. The purpose of this study was to develop and validate a novel depth estimation algorithm based on a smartwatch equipped with a built-in accelerometer for feedback instructions during CPR.

METHODS

For data collection and model building, researchers wore an Android Wear smartwatch and performed chest compression-only CPR on a Resusci Anne QCPR training manikin. We developed an algorithm based on the assumptions that (1) maximal acceleration measured by the smartwatch accelerometer and the chest compression depth (CCD) are positively correlated and (2) the magnitude of acceleration at a specific time point and interval is correlated with its neighboring points. We defined a statistic value M as a function of time and the magnitude of maximal acceleration. We labeled and processed collected data and determined the relationship between M value, compression rate and CCD. We built a model accordingly, and developed a smartwatch app capable of detecting CCD. For validation, researchers wore a smartwatch with the preinstalled app and performed chest compression-only CPR on the manikin at target sessions. We compared the CCD results given by the smartwatch and the reference using the Wilcoxon Signed Rank Test (WSRT), and used Bland-Altman (BA) analysis to assess the agreement between the two methods.

RESULTS

We analyzed a total of 3978 compressions that covered the target rate of 80-140/min and CCD of 4-7 cm. WSRT showed that there was no significant difference between the two methods (P = 0.084). By BA analysis the mean of differences was 0.003 and the bias between the two methods was not significant (95% CI: -0.079 to 0.085).

CONCLUSION

Our study indicates that the algorithm developed for estimating CCD based on a smartwatch with a built-in accelerometer is promising. Further studies will be conducted to evaluate its application for CPR training and clinical practice.

Monitoring chest compression quality during cardiopulmonary resuscitation: Proof-of-concept of a single accelerometer-based feedback algorithm

BACKGROUND

The use of real-time feedback systems to guide rescuers during cardiopulmonary resuscitation (CPR) significantly contributes to improve adherence to published resuscitation guidelines. Recently, we designed a novel method for computing depth and rate of chest compressions relying solely on the spectral analysis of chest acceleration. That method was extensively tested in a simulated manikin scenario. The purpose of this study is to report the results of this method as tested in human out-of-hospital cardiac arrest (OHCA) cases.

MATERIALS AND METHODS

The algorithm was evaluated retrospectively with seventy five OHCA episodes recorded by monitor-defibrillators equipped with a CPR feedback device. The acceleration signal and the compression signal computed by the CPR feedback device were stored in each episode. The algorithm was continuously applied to the acceleration signals. The depth and rate values estimated every 2-s from the acceleration data were compared to the reference values obtained from the compression signal. The performance of the algorithm was assesed in terms of the sensitivity and positive predictive value (PPV) for detecting compressions and in terms of its accuracy through the analysis of measurement error.

RESULTS

The algorithm reported a global sensitivity and PPV of 99.98% and 99.79%, respectively. The median (P75) unsigned error in depth and rate was 0.9 (1.7) mm and 1.0 (1.7) cpm, respectively. In 95% of the analyzed 2-s windows the error was below 3.5 mm and 3.1 cpm, respectively.

CONCLUSIONS

The CPR feedback algorithm proved to be reliable and accurate when tested retrospectively with human OHCA episodes. A new CPR feedback device based on this algorithm could be helpful in the resuscitation field.

A Feasibility Study for Measuring Accurate Chest Compression Depth and Rate on Soft Surfaces Using Two Accelerometers and Spectral Analysis

Background. Cardiopulmonary resuscitation (CPR) feedback devices are being increasingly used. However, current accelerometer-based devices overestimate chest displacement when CPR is performed on soft surfaces, which may lead to insufficient compression depth. Aim. To assess the performance of a new algorithm for measuring compression depth and rate based on two accelerometers in a simulated resuscitation scenario. Materials and Methods. Compressions were provided to a manikin on two mattresses, foam and sprung, with and without a backboard. One accelerometer was placed on the chest and the second at the manikin's back. Chest displacement and mattress displacement were calculated from the spectral analysis of the corresponding acceleration every 2 seconds and subtracted to compute the actual sternal-spinal displacement. Compression rate was obtained from the chest acceleration. Results. Median unsigned error in depth was 2.1 mm (4.4%). Error was 2.4 mm in the foam and 1.7 mm in the sprung mattress (p < 0.001). Error was 3.1/2.0 mm and 1.8/1.6 mm with/without backboard for foam and sprung, respectively (p < 0.001). Median error in rate was 0.9 cpm (1.0%), with no significant differences between test conditions. Conclusion. The system provided accurate feedback on chest compression depth and rate on soft surfaces. Our solution compensated mattress displacement, avoiding overestimation of compression depth when CPR is performed on soft surfaces.

Feedback on the Rate and Depth of Chest Compressions during Cardiopulmonary Resuscitation Using Only Accelerometers

Background

Quality of cardiopulmonary resuscitation (CPR) is key to increase survival from cardiac arrest. Providing chest compressions with adequate rate and depth is difficult even for well-trained rescuers. The use of real-time feedback devices is intended to contribute to enhance chest compression quality. These devices are typically based on the double integration of the acceleration to obtain the chest displacement during compressions. The integration process is inherently unstable and leads to important errors unless boundary conditions are applied for each compression cycle. Commercial solutions use additional reference signals to establish these conditions, requiring additional sensors. Our aim was to study the accuracy of three methods based solely on the acceleration signal to provide feedback on the compression rate and depth.

Materials and Methods

We simulated a CPR scenario with several volunteers grouped in couples providing chest compressions on a resuscitation manikin. Different target rates (80, 100, 120, and 140 compressions per minute) and a target depth of at least 50 mm were indicated. The manikin was equipped with a displacement sensor. The accelerometer was placed between the rescuer’s hands and the manikin’s chest. We designed three alternatives to direct integration based on different principles (linear filtering, analysis of velocity, and spectral analysis of acceleration). We evaluated their accuracy by comparing the estimated depth and rate with the values obtained from the reference displacement sensor.

Results

The median (IQR) percent error was 5.9% (2.8–10.3), 6.3% (2.9–11.3), and 2.5% (1.2–4.4) for depth and 1.7% (0.0–2.3), 0.0% (0.0–2.0), and 0.9% (0.4–1.6) for rate, respectively. Depth accuracy depended on the target rate (p < 0.001) and on the rescuer couple (p < 0.001) within each method.

Conclusions

Accurate feedback on chest compression depth and rate during CPR is possible using exclusively the chest acceleration signal. The algorithm based on spectral analysis showed the best performance. Despite these encouraging results, further research should be conducted to asses the performance of these algorithms with clinical data.

A simple accurate chest-compression depth gauge using magnetic coils during cardiopulmonary resuscitation

This paper describes a new method for calculating chest compression depth and a simple chest-compression gauge for validating the accuracy of the method. The chest-compression gauge has two plates incorporating two magnetic coils, a spring, and an accelerometer. The coils are located at both ends of the spring, and the accelerometer is set on the bottom plate. Waveforms obtained using the magnetic coils (hereafter, "magnetic waveforms"), which are proportional to compression-force waveforms and the acceleration waveforms were measured at the same time. The weight factor expressing the relationship between the second derivatives of the magnetic waveforms and the measured acceleration waveforms was calculated. An estimated-compression-displacement (depth) waveform was obtained by multiplying the weight factor and the magnetic waveforms. Displacements of two large springs (with similar spring constants) within a thorax and displacements of a cardiopulmonary resuscitation training manikin were measured using the gauge to validate the accuracy of the calculated waveform. A laser-displacement detection system was used to compare the real displacement waveform and the estimated waveform. Intraclass correlation coefficients (ICCs) between the real displacement using the laser system and the estimated displacement waveforms were calculated. The estimated displacement error of the compression depth was within 2 mm (<1 standard deviation). All ICCs (two springs and a manikin) were above 0.85 (0.99 in the case of one of the springs). The developed simple chest-compression gauge, based on a new calculation method, provides an accurate compression depth (estimation error < 2 mm).