8,954 research outputs found
Feedback systems for the quality of chest compressions during cardiopulmonary resuscitation
167 p.Se define la parada cardiorrespiratoria como la detención súbita de la actividad mecánica del corazón, confirmada por la ausencia de signos de circulación. En caso de parada cardiorrespiratoria, dos actuaciones son clave para la supervivencia del paciente: la reanimación cardiopulmonar (RCP) precoz, y la desfibrilación precoz. La RCP consiste en proporcionar compresiones torácicas y ventilaciones al paciente para mantener un mínimo flujo de sangre oxigenada a los órganos vitales. La calidad de las compresiones está relacionada con la supervivencia del paciente. Por esta razón las guías de resucitación recomiendan el uso de sistemas de feedback que monitorizan la calidad de la RCP en tiempo real. Estos dispositivos se sitúan generalmente entre el pecho del paciente y las manos del rescatador, y guían al rescatador para ayudarle a alcanzar la profundidad y frecuencia de compresión objetivo. Esta tesis explora nuevas alternativas para monitorizar la calidad de las compresiones durante la RCP. Se han seguido dos estrategias: usar la señal de impedancia transtorácica (ITT), que es adquirida por los desfibriladores actuales a través de los parches de desfibrilación, y usar la aceleración del pecho, que podría ser registrada usando un dispositivo adicional
Ventricular Fibrillation Waveform Analysis during Cardiopulmonary Resuscitation
Ventricular fibrillation (VF) is the primary rhythm associated with cardiac arrest characterized as rapid, disorganized contractions
of the heart with complex electrocardiogram (ECG) patterns. Recent studies have reported that performing cardiopulmonary
resuscitation (CPR) procedure prior to shock increases the survival rate especially especially when VF is untreated
for more than 5 minutes. The waveform analysis is objective help in the choice of the right therapy (shock parameters, shock
first or CPR first, drug administration). This analysis is a precondition of individually optimized defibrillation and contribute
substantially to an increased quality of CPR and reduce delivery of failed rescue shock. Animal and clinical studies confirmed
that ventricular fibrillation waveform analysis contains information to reliably predict the countershock success rate
and further improved countershock outcome prediction
Waveform Capnography for Monitoring Ventilation during Cardiopulmonary Resuscitation: The Problem of Chest Compression Artifact
Sudden cardiac arrest (SCA) is the sudden cessation of the heart’s effective pumping function, confirmed by the absence of pulse and breathing. Without appropriate treatment, it leads to sudden cardiac death, considered responsible for half of the global cardiac disease deaths. Cardiopulmonary resuscitation (CPR) is a key intervention during SCA. Current resuscitation guidelines emphasize the use of waveform capnography during CPR in order to enhance CPR quality and improve patient outcomes. Capnography represents the concentration of the partial pressure of carbon dioxide (CO2) in respiratory gases and reflects ventilation and perfusion of the patient. Waveform capnography should be used for confirming the correct placement of the tracheal tube and monitoring ventilation. Other potential uses of capnography in resuscitation involve monitoring CPR quality, early identification of restoration of spontaneous circulation (ROSC), and determination of patient prognosis. An important role of waveform capnography is ventilation rate monitoring to prevent overventilation. However, some studies have reported the appearance of high-frequency oscillations synchronized with chest compressions superimposed on the capnogram. This chapter explores the incidence of chest compression artifact in out-of-hospital capnograms, assesses its negative influence in the automated detection of ventilations, and proposes several methods to enhance ventilation detection and capnography waveform
Effect Of The Cardio First Angel™ Device On CPR Indices: A Randomized Controlled Clinical Trial
Background: A number of cardiopulmonary resuscitation (CPR) adjunct devices have been developed to improve the consistency and quality of manual chest compressions. We investigated whether a CPR feedback device would improve CPR quality and consistency, as well as patient survival.
Methods: We conducted a randomized controlled study of patients undergoing CPR for cardiac arrest in the mixed medical-surgical intensive care units of four academic teaching hospitals. Patients were randomized to receive either standard manual CPR or CPR using the Cardio First Angel™ CPR feedback device. Recorded variables included guideline adherence, CPR quality, return of spontaneous circulation (ROSC) rates, and CPR-associated morbidity.
Results: A total of 229 subjects were randomized; 149 were excluded; and 80 were included. Patient demographics were similar. Adherence to published CPR guidelines and CPR quality was significantly improved in the intervention group (p \u3c 0.0001), as were ROSC rates (72 % vs. 35 %; p = 0.001). A significant decrease was observed in rib fractures (57 % vs. 85 %; p = 0.02), but not sternum fractures (5 % vs. 17 %; p = 0.15).
Conclusions: Use of the Cardio First Angel™ CPR feedback device improved adherence to published CPR guidelines and CPR quality, and it was associated with increased rates of ROSC. A decrease in rib but not sternum fractures was observed with device use. Further independent prospective validation is warranted to determine if these results are reproducible in other acute care settings
Biomechanics
Biomechanics is a vast discipline within the field of Biomedical Engineering. It explores the underlying mechanics of how biological and physiological systems move. It encompasses important clinical applications to address questions related to medicine using engineering mechanics principles. Biomechanics includes interdisciplinary concepts from engineers, physicians, therapists, biologists, physicists, and mathematicians. Through their collaborative efforts, biomechanics research is ever changing and expanding, explaining new mechanisms and principles for dynamic human systems. Biomechanics is used to describe how the human body moves, walks, and breathes, in addition to how it responds to injury and rehabilitation. Advanced biomechanical modeling methods, such as inverse dynamics, finite element analysis, and musculoskeletal modeling are used to simulate and investigate human situations in regard to movement and injury. Biomechanical technologies are progressing to answer contemporary medical questions. The future of biomechanics is dependent on interdisciplinary research efforts and the education of tomorrow’s scientists
Fog Computing in Medical Internet-of-Things: Architecture, Implementation, and Applications
In the era when the market segment of Internet of Things (IoT) tops the chart
in various business reports, it is apparently envisioned that the field of
medicine expects to gain a large benefit from the explosion of wearables and
internet-connected sensors that surround us to acquire and communicate
unprecedented data on symptoms, medication, food intake, and daily-life
activities impacting one's health and wellness. However, IoT-driven healthcare
would have to overcome many barriers, such as: 1) There is an increasing demand
for data storage on cloud servers where the analysis of the medical big data
becomes increasingly complex, 2) The data, when communicated, are vulnerable to
security and privacy issues, 3) The communication of the continuously collected
data is not only costly but also energy hungry, 4) Operating and maintaining
the sensors directly from the cloud servers are non-trial tasks. This book
chapter defined Fog Computing in the context of medical IoT. Conceptually, Fog
Computing is a service-oriented intermediate layer in IoT, providing the
interfaces between the sensors and cloud servers for facilitating connectivity,
data transfer, and queryable local database. The centerpiece of Fog computing
is a low-power, intelligent, wireless, embedded computing node that carries out
signal conditioning and data analytics on raw data collected from wearables or
other medical sensors and offers efficient means to serve telehealth
interventions. We implemented and tested an fog computing system using the
Intel Edison and Raspberry Pi that allows acquisition, computing, storage and
communication of the various medical data such as pathological speech data of
individuals with speech disorders, Phonocardiogram (PCG) signal for heart rate
estimation, and Electrocardiogram (ECG)-based Q, R, S detection.Comment: 29 pages, 30 figures, 5 tables. Keywords: Big Data, Body Area
Network, Body Sensor Network, Edge Computing, Fog Computing, Medical
Cyberphysical Systems, Medical Internet-of-Things, Telecare, Tele-treatment,
Wearable Devices, Chapter in Handbook of Large-Scale Distributed Computing in
Smart Healthcare (2017), Springe
- …