Abstract：
BACKGROUND: The analysis of EEG demands expertise and keen observation to distinguish epileptiform discharges from benign epileptiform variants (BEVs), a frequent source of erroneous interpretation. The prevalence of BEVs varies based on geographical, racial, and ethnic characteristics. However, most data on BEVs originates from Western populations, and additional studies on different cohorts would enrich the existing literature.
METHODS: We reviewed EEGs from our institutional database to study the prevalence of benign epileptiform variants and analyzed their frequency, topography, and other characteristics. Additionally, we investigated the co-existence of epileptiform discharges with BEVs.
RESULTS: We identified 296 patients with BEVs after reviewing 3000 EEGs (9.9%). The most common BEV was small sharp spikes (SSS), observed in 114 patients (3.8%). Wicket waves, 6 Hz spike and slow wave, 14 and 6 Hz positive bursts, and Rhythmic Temporal Theta of Drowsiness (RTTD) were identified in 67 (2.2%), 40 (1.3%), 39 (1.3%), and 35 (1.16%) patients, respectively and one patient with Subclinical Rhythmic EEG Discharges in Adults (SREDA). Additionally, we observed the co-existence of epileptiform discharges with BEVs, most commonly with SSS (27.8%).
CONCLUSIONS: The present study is a large study with 3000 EEGs to describe the BEV characteristics. BEVs were seen in 9.9% of patients, BSSS being the most common. There were minor differences in frequency, gender or age distribution compared to existing literature. We demonstrated the co-existence of epileptiform discharges. Morphological characteristics remain the cornerstone in recognising BEVs. EEG readers need to be aware of features of BEVs to avoid wrongly interpretation.
METHODS: We reviewed EEGs from our institutional database to study the prevalence of benign epileptiform variants and analyzed their frequency, topography, and other characteristics. Additionally, we investigated the co-existence of epileptiform discharges with BEVs.
RESULTS: We identified 296 patients with BEVs after reviewing 3000 EEGs (9.9%). The most common BEV was small sharp spikes (SSS), observed in 114 patients (3.8%). Wicket waves, 6 Hz spike and slow wave, 14 and 6 Hz positive bursts, and Rhythmic Temporal Theta of Drowsiness (RTTD) were identified in 67 (2.2%), 40 (1.3%), 39 (1.3%), and 35 (1.16%) patients, respectively and one patient with Subclinical Rhythmic EEG Discharges in Adults (SREDA). Additionally, we observed the co-existence of epileptiform discharges with BEVs, most commonly with SSS (27.8%).
CONCLUSIONS: The present study is a large study with 3000 EEGs to describe the BEV characteristics. BEVs were seen in 9.9% of patients, BSSS being the most common. There were minor differences in frequency, gender or age distribution compared to existing literature. We demonstrated the co-existence of epileptiform discharges. Morphological characteristics remain the cornerstone in recognising BEVs. EEG readers need to be aware of features of BEVs to avoid wrongly interpretation.
摘要：
背景：脑电图分析需要专业知识和敏锐的观察才能区分癫痫样放电与良性癫痫样变异（BEV），错误解释的频繁来源。BEV的患病率因地域而异，种族,和民族特色。然而,关于BEV的大多数数据来自西方人群，对不同队列的其他研究将丰富现有文献。
方法：我们回顾了我们的机构数据库中的脑电图，以研究良性癫痫样变异的患病率，并分析了其频率，地形,和其他特征。此外,我们调查了癫痫样放电与BEV的共存。
结果：我们在回顾了3000例脑电图（9.9％）后确定了296例BEV患者。最常见的BEV是小尖刺（SSS），在114例患者中观察到（3.8％）。Wicket波浪，6Hz尖峰和慢波，14和6Hz正脉冲串，和节律性的昏睡性时间θ（RTTD）在67（2.2％）中被确定，40（1.3%），39（1.3%），35名(1.16%)患者，分别和一名成人亚临床节律性脑电图放电(SREDA)患者。此外,我们观察到癫痫样放电与BEV共存，最常见的是SSS（27.8%）。
结论：本研究是一项大型研究，使用3000个EEG来描述BEV特征。在9.9％的患者中观察到BEV，BSSS是最常见的。频率有微小的差异，与现有文献相比，性别或年龄分布。我们证明了癫痫样放电的共存。形态特征仍然是识别BEV的基石。脑电图读者需要了解BEV的特征，以避免错误的解释。
方法：我们回顾了我们的机构数据库中的脑电图，以研究良性癫痫样变异的患病率，并分析了其频率，地形,和其他特征。此外,我们调查了癫痫样放电与BEV的共存。
结果：我们在回顾了3000例脑电图（9.9％）后确定了296例BEV患者。最常见的BEV是小尖刺（SSS），在114例患者中观察到（3.8％）。Wicket波浪，6Hz尖峰和慢波，14和6Hz正脉冲串，和节律性的昏睡性时间θ（RTTD）在67（2.2％）中被确定，40（1.3%），39（1.3%），35名(1.16%)患者，分别和一名成人亚临床节律性脑电图放电(SREDA)患者。此外,我们观察到癫痫样放电与BEV共存，最常见的是SSS（27.8%）。
结论：本研究是一项大型研究，使用3000个EEG来描述BEV特征。在9.9％的患者中观察到BEV，BSSS是最常见的。频率有微小的差异，与现有文献相比，性别或年龄分布。我们证明了癫痫样放电的共存。形态特征仍然是识别BEV的基石。脑电图读者需要了解BEV的特征，以避免错误的解释。
