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Sleep Apnea Syndromes: HELP
Articles by Pengbo Jiang
Based on 3 articles published since 2010
(Why 3 articles?)
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Between 2010 and 2020, Peng Jiang wrote the following 3 articles about Sleep Apnea Syndromes.
 
+ Citations + Abstracts
1 Clinical Trial Combination mode of physiological signals for diagnosis of OSAS using portable monitor. 2018

Jiang, Peng / Zhu, Rong / Dong, Xiaosong / Chang, Yuan. ·State Key Laboratory of Precision Measurement Technology and Instrument, Department of Precision Instrument, Tsinghua University, Haidian District, Beijing, 100084, China. · State Key Laboratory of Precision Measurement Technology and Instrument, Department of Precision Instrument, Tsinghua University, Haidian District, Beijing, 100084, China. zr_gloria@mail.tsinghua.edu.cn. · Sleep Center, Department of Pulmonary and Critical Care Medicine, Peking University People's Hospital, No. 11 South Street, Xizhimen, Beijing, 100044, China. ·Sleep Breath · Pubmed #28744805.

ABSTRACT: PURPOSE: Portable respiratory monitor (PRM) has been proposed for pre-diagnosis of obstructive sleep apnea syndrome (OSAS). However, discrepant physiological signal combinations were rarely studied for diagnostic assessment of OSAS. This study was designed to evaluate combination modes of key physiological signals collected by portable sensor modules for OSAS screening in comparison with polysomnography (PSG). METHODS: People with suspected OSAS were submitted to PRM at a sleep laboratory monitoring concurrently with PSG. The diagnostic accuracy was assessed by sensitivity, specificity, Pearson correlation coefficients, kappa statistic, and Bland-Altman plot. Four combination modes of PRM, including mode 1 with single nasal airflow, mode 2 with airflow plus body activity, mode 3 with airflow plus SpO2, tri-combination mode 4 with airflow plus SpO2 plus activity were studied. RESULTS: Thirty-five subjects (69% men, mean age ± SD, 49 ± 12 years) with averaged apnea-hypopnea index (AHI) of 36 ± 29 events/h were tested. Excluding incomplete recordings, 33 valid samples were analyzed. All PRM modes demonstrated good concordances with PSG in diagnostic outcomes. Tri-combination mode had optimum with sensitivity of 96.5%, specificity of 100%, +LR of 4, -LR of 0.03, and kappa coefficient of 0.85 for screening OSAS holding AHI ≥5. Its Bland-Altman plots also showed the smallest dispersion. CONCLUSIONS: This study used clinical comparison to demonstrate diagnostic accuracy of PRM with different physiological signal combination. The combination of respiratory airflow, oxygen saturation, and body activity provided sufficiently high accuracy for diagnosing OSAS. Single respiratory airflow sensor as the simplest PRM was also feasible for pre-screening OSAS.

2 Article Patients presenting to a Men's Health clinic are at higher risk for depression, insomnia, and sleep apnea. 2019

Walia, Arman S / Lomeli, Luis de Jesus Martinez / Jiang, Pengbo / Benca, Ruth / Yafi, Faysal A. ·Department of Urology, University of California-Irvine, Irvine, CA, USA. · Center for Complex Biological Systems, University of California-Irvine, Irvine, CA, USA. · Department of Psychiatry & Human Behavior, University of California-Irvine, Irvine, CA, USA. · Department of Urology, University of California-Irvine, Irvine, CA, USA. faysalyafi@gmail.com. ·Int J Impot Res · Pubmed #30171191.

ABSTRACT: Depression and sleep problems are highly prevalent disorders that are often comorbid with other medical disorders. We evaluated the prevalence and associations of these conditions in patients presenting to a Men's Health clinic. In this retrospective study, 124 patients presenting to a Men's Health clinic completed three urological questionnaires (International Index of Erectile Function [IIEF-5], International Prostate Symptom Score [IPSS], and Androgen Deficiency in Aging Males [ADAM]); and four non-urological questionnaires (Patient Health Questionnaire for depression [PHQ-9], STOP-BANG Sleep Apnea [OSA STOP-BANG], Insomnia Severity Index [ISI], and Epworth Sleepiness Scale [ESS]). Questionnaire results were evaluated in conjunction with patient clinical history and associated laboratory values via univariate and multivariate analysis. The mean age of the study participants was 54.1 years (SD 16). Comorbidities included hypertension (22.5%), vascular disease (15%), and diabetes mellitus (13.3%). Body Mass Index (BMI) was >25 in 77.3%. IIEF-5 scores were moderate-severe in 47.9%, ADAM questionnaire was positive in 79%, and IPSS scores were moderate-severe in 42.9% of patients. PHQ-9 demonstrated mild-severe depression in 38.6%, STOP-BANG showed intermediate-high risk for sleep apnea in 55.2%, ISI indicated moderate-severe insomnia in 18.1%, and ESS revealed mild-severe sleepiness in 16.6% of participants. On univariate analysis, BMI was associated with scores on the PHQ-9 (p = 0.035), STOP-BANG (p < 0.001), and ESS (p < 0.006). On multivariate analysis, positive ADAM questionnaire was associated with STOP-BANG (OR 3.29, 95% CI: 1.012-10.69), and IPSS with PHQ-9 (OR 4.64, 95% CI: 1.40-15.43) and ISI (OR 3.27, 95% CI: 1.06-10.1). Overall, patients presenting to a Men's Health Clinic were found to have high prevalence rates for risk of depression, insomnia and sleep apnea. Risks were elevated in older subjects, and those with increased BMI, hypogonadism, and lower urinary tract symptoms. Appropriate screening and referral to appropriate specialists are encouraged.

3 Article Elimination of Drifts in Long-Duration Monitoring for Apnea-Hypopnea of Human Respiration. 2016

Jiang, Peng / Zhu, Rong. ·State Key Laboratory of Precision Measurement Technology and Instrument, Department of Precision Instrument, Tsinghua University, Beijing 100084, China. jiang-p13@mails.tsinghua.edu.cn. · State Key Laboratory of Precision Measurement Technology and Instrument, Department of Precision Instrument, Tsinghua University, Beijing 100084, China. zr_gloria@mail.tsinghua.edu.cn. ·Sensors (Basel) · Pubmed #27792151.

ABSTRACT: This paper reports a methodology to eliminate an uncertain baseline drift in respiratory monitoring using a thermal airflow sensor exposed in a high humidity environment. Human respiratory airflow usually contains a large amount of moisture (relative humidity, RH > 85%). Water vapors in breathing air condense gradually on the surface of the sensor so as to form a thin water film that leads to a significant sensor drift in long-duration respiratory monitoring. The water film is formed by a combination of condensation and evaporation, and therefore the behavior of the humidity drift is complicated. Fortunately, the exhale and inhale responses of the sensor exhibit distinguishing features that are different from the humidity drift. Using a wavelet analysis method, we removed the baseline drift of the sensor and successfully recovered the respiratory waveform. Finally, we extracted apnea-hypopnea events from the respiratory signals monitored in whole-night sleeps of patients and compared them with golden standard polysomnography (PSG) results.