title: Development of three methods for extracting respiration from the surface ECG: a review.
abstract: BACKGROUND
  Respiration rate (RR) is a critical vital sign that can be monitored to detect acute changes in patient condition (e.g., apnea) and potentially provide an early warning of impending life-threatening deterioration. Monitoring respiration signals is also critical for detecting sleep disordered breathing such as sleep apnea. Additionally, analyzing a respiration signal can enhance the quality of medical images by gating image acquisition based on the same phase of the patient's respiratory cycle. Although many methods exist for measuring respiration, in this review we focus on three ECG-derived respiration techniques we developed to obtain respiration from an ECG signal.
  
  
  METHODS
  The first step in all three techniques is to analyze the ECG to detect beat locations and classify them. 1) The EDR method is based on analyzing the heart axis shift due to respiration. In our method, one respiration waveform value is calculated for each normal QRS complex by measuring the peak to QRS trough amplitude. Compared to other similar EDR techniques, this method does not need removal of baseline wander from the ECG signal. 2) The RSA method uses instantaneous heart rate variability to derive a respiratory signal. It is based on the observed respiratory sinus arrhythmia governed by baroreflex sensitivity. 3) Our EMGDR method for computing a respiratory waveform uses measurement of electromyogram (EMG) activity created by respiratory effort of the intercostal muscles and diaphragm. The ECG signal is high-pass filtered and processed to reduce ECG components and accentuate the EMG signal before applying RMS and smoothing.
  
  
  RESULTS
  Over the last five years, we have performed six studies using the above methods: 1) In 1907 sleep lab patients with >1.5M 30-second epochs, EDR achieved an apnea detection accuracy of 79%. 2) In 24 adult polysomnograms, use of EDR and chest belts for RR computation was compared to airflow RR; mean RR error was EDR: 1.8±2.7 and belts: 0.8±2.1. 3) During cardiac MRI, a comparison of EMGDR breath locations to the reference abdominal belt signal yielded sensitivity/PPV of 94/95%. 4) Another comparison study for breath detection during MRI yielded sensitivity/PPV pairs of EDR: 99/97, RSA: 79/78, and EMGDR: 89/86%. 5) We tested EMGDR performance in the presence of simulated respiratory disease using CPAP to produce PEEP. For 10 patients, no false breath waveforms were generated with mild PEEP, but they appeared in 2 subjects at high PEEP. 6) A patient monitoring study compared RR computation from EDR to impedance-derived RR, and showed that EDR provides a near equivalent RR measurement with reduced hardware circuitry requirements.
authors.affiliation: Advanced Algorithm Research Center, Philips Healthcare, Andover, MA, USA. Electronic address: eric.helfenbein@philips.com.
authors.forename: Eric
authors.initials: E
authors.lastname: Helfenbein
authors.affiliation: Advanced Algorithm Research Center, Philips Healthcare, Andover, MA, USA.
authors.forename: Reza
authors.initials: R
authors.lastname: Firoozabadi
authors.affiliation: Advanced Algorithm Research Center, Philips Healthcare, Andover, MA, USA.
authors.forename: Simon
authors.initials: S
authors.lastname: Chien
authors.affiliation: Philips Research North America, Briarcliff Manor, NY, USA.
authors.forename: Eric
authors.initials: E
authors.lastname: Carlson
authors.affiliation: Advanced Algorithm Research Center, Philips Healthcare, Andover, MA, USA.
authors.forename: Saeed
authors.initials: S
authors.lastname: Babaeizadeh
mesh_terms: D000465:Algorithms; D001049:Apnea / Q000175:diagnosis*; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D004562:Electrocardiography / Q000379:methods*; D004576:Electromyography / Q000379:methods*; D006801:Humans; D010363:Pattern Recognition, Automated / Q000379:methods; D017286:Polysomnography / Q000379:methods; D015203:Reproducibility of Results; D056152:Respiratory Rate; D012680:Sensitivity and Specificity
keywords: ECG derived respiration; EMG derived respiration; Respiration gating; Respiratory sinus arrhythmia derived respiration; Sleep apnea detection